From prediction to causation: Causal inference in online systems
•Download as PPTX, PDF•
1 like•2,811 views
Predictive modeling and machine learning has been widely successful: such models can suggest movies, songs or games to try out, identify people to target ads for, predict customer churn, detect fraud, identify health risks and so on. However, predictive models are not well-equipped to answer questions about cause and effect, which form the logical next step of action after establishing correlational models: what should one do next to improve key metrics or goals? For example, what is the effect of a recommender algorithm? if a product recommendation system is changed or removed, what will be the effect on people's purchases? If having more friends is correlated with higher activity on a social network, would encouraging users to add more friends increase their activity? This tutorial won't give all the answers, but will provide a principled way to reason about causal effects and estimate them. In the first half of the tutorial, I will present an overview of counterfactual reasoning and common methods for causal inference. The second half is hands-on: a practical example of estimating the causal impact of a recommender system, starting from simple methods to more complex methods, with the side-goal of appreciating and learning from common pitfalls in causal inference. Code and resources for the tutorial available at: https://github.com/amit-sharma/causal-inference-tutorial/ Amit Sharma should we focus on encouraging Xbox users to add more friends? In the first half of the tutorial, I will present an overview of counterfactual reasoning and common methods for causal inference. In the second half, participants will work through a practical example of estimating the causal impact of a recommender system, starting from simple methods to more complex methods, with the side-goal of appreciating and learning from common pitfalls in causal inference.
Report
Share
Report
Share
Recommended
Causal inference in online systems: Methods, pitfalls and best practices
From recommending what to buy, which movies to watch, to selecting the news to read, people to follow and jobs to apply for, online systems have become an important part of our daily lives. A natural question to ask is how these socio-technical systems impact our behavior. However, because of the intricate interplay between the outputs of these systems and people's actions, identifying their impact on people's behavior is non-trivial.
Fortunately, there is a rich body of work on causal inference that we can build on. In the first part of the tutorial, I will show the value of counterfactual reasoning for studying socio-technical systems, by demonstrating how predictive modeling based on correlations can be counterproductive. Then, we will discuss different approaches to causal inference, including randomized experiments, natural experiments such as instrumental variables and regression discontinuities, and observational methods such as stratification and matching. Throughout, we will try to make connections with graphical models, machine learning and past work in the social sciences.
The second half will be more hands-on. We will work through a practical example of estimating the causal impact of a recommender system, starting from simple to more complex methods. The goal of the practical exercise will be to appreciate the pitfalls in different approaches to causal reasoning and take away best practices for doing causal inference with messy, real-world data.
Code used is available at: https://github.com/amit-sharma/causal-inference-tutorial/
Data mining for causal inference: Effect of recommendations on Amazon.com
As an increasing amount of daily activity---ranging from what we purchase to who we talk---shifts to online platforms, it is only natural to ask how those platforms impact our behavior. Take, for instance, online recommendation systems: how much activity do recommendations actually cause over and above what would have happened in their absence? Without doing randomized experiments, which may be costly or infeasible, estimating the impact of such systems is non-trivial. In this talk, I will argue that careful data mining can help in answering relevant causal questions in a more general way than traditional observational approaches.
Taking recommender systems as an example domain, I will show that data mining can be used to augment a popular techniques such as instrumental variables, by searching for large and sudden shocks in time series data. Applying this method to system logs for Amazon's "People who bought this also bought" recommendations, we are able to analyze over 4,000 unique products that experience such shocks. This leads to a more accurate estimate of the impact of the recommender system: at least 75% of recommendation click-throughs would likely occur in their absence, questioning popular industry estimates based on observed click-through rates.
Finally, this shock-based approach can be generalized to derive a data-driven identification strategy for finding natural experiments in time series data. This method too reveals a similar overestimate for the impact of recommendation systems.
Causal inference in data science
The document discusses using correlations to build predictive models and compares the success rates of two algorithms, Algorithm A and Algorithm B, on different user groups. It also notes that while average comment length decreases over time across all users, length increases over time for each yearly cohort. References are provided on causation and spurious correlations.
Causal inference in practice
An introduction to causal graphical models with examples of causality in practice from different fields of science. More focused discussion of causal inference in online ads and recommender systems.
Updated modifications to the HIPAA Privacy Rule
This webinar addresses updates to HIPAA Privacy, Security, and Breach Notification standards identified in the most recent legislation
(New) final exam for acc 421 all correct answers 100%
This document provides the questions and answers to an ACC/421 final exam. It contains 30 multiple choice questions testing concepts related to accounting, financial statements, ratios, and cash flow statements. The document encourages the reader to leave positive feedback if it helped them and wishes them good luck on their exam.
(New) final exam for mgt 521 all correct answers 100%
This document provides the questions and answers to a multiple choice exam on management and business topics. It covers areas like communication, marketing, leadership, ethics, entrepreneurship, accounting, economics, human resources, and strategic planning. The questions test understanding of key concepts in these areas and ability to identify examples and apply definitions. The document suggests the answers may not completely cover the actual exam being taken.
Criminología i concepto objeto de estudio y entidad científica6
Este documento discute el concepto, objeto de estudio y entidad científica de la criminología. Explica que la criminología surgió en el siglo XIX para estudiar el comportamiento antisocial siguiendo el método científico con el fin de prevenir el crimen. Aunque hay diferentes definiciones de criminología, se define generalmente como la ciencia que aplica el método empírico al estudio del comportamiento antisocial y las reacciones sociales. El objeto de estudio incluye las causas de la delincuencia, el delincuente
Recommended
Causal inference in online systems: Methods, pitfalls and best practices
From recommending what to buy, which movies to watch, to selecting the news to read, people to follow and jobs to apply for, online systems have become an important part of our daily lives. A natural question to ask is how these socio-technical systems impact our behavior. However, because of the intricate interplay between the outputs of these systems and people's actions, identifying their impact on people's behavior is non-trivial.
Fortunately, there is a rich body of work on causal inference that we can build on. In the first part of the tutorial, I will show the value of counterfactual reasoning for studying socio-technical systems, by demonstrating how predictive modeling based on correlations can be counterproductive. Then, we will discuss different approaches to causal inference, including randomized experiments, natural experiments such as instrumental variables and regression discontinuities, and observational methods such as stratification and matching. Throughout, we will try to make connections with graphical models, machine learning and past work in the social sciences.
The second half will be more hands-on. We will work through a practical example of estimating the causal impact of a recommender system, starting from simple to more complex methods. The goal of the practical exercise will be to appreciate the pitfalls in different approaches to causal reasoning and take away best practices for doing causal inference with messy, real-world data.
Code used is available at: https://github.com/amit-sharma/causal-inference-tutorial/
Data mining for causal inference: Effect of recommendations on Amazon.com
As an increasing amount of daily activity---ranging from what we purchase to who we talk---shifts to online platforms, it is only natural to ask how those platforms impact our behavior. Take, for instance, online recommendation systems: how much activity do recommendations actually cause over and above what would have happened in their absence? Without doing randomized experiments, which may be costly or infeasible, estimating the impact of such systems is non-trivial. In this talk, I will argue that careful data mining can help in answering relevant causal questions in a more general way than traditional observational approaches.
Taking recommender systems as an example domain, I will show that data mining can be used to augment a popular techniques such as instrumental variables, by searching for large and sudden shocks in time series data. Applying this method to system logs for Amazon's "People who bought this also bought" recommendations, we are able to analyze over 4,000 unique products that experience such shocks. This leads to a more accurate estimate of the impact of the recommender system: at least 75% of recommendation click-throughs would likely occur in their absence, questioning popular industry estimates based on observed click-through rates.
Finally, this shock-based approach can be generalized to derive a data-driven identification strategy for finding natural experiments in time series data. This method too reveals a similar overestimate for the impact of recommendation systems.
Causal inference in data science
The document discusses using correlations to build predictive models and compares the success rates of two algorithms, Algorithm A and Algorithm B, on different user groups. It also notes that while average comment length decreases over time across all users, length increases over time for each yearly cohort. References are provided on causation and spurious correlations.
Causal inference in practice
An introduction to causal graphical models with examples of causality in practice from different fields of science. More focused discussion of causal inference in online ads and recommender systems.
Updated modifications to the HIPAA Privacy Rule
This webinar addresses updates to HIPAA Privacy, Security, and Breach Notification standards identified in the most recent legislation
(New) final exam for acc 421 all correct answers 100%
This document provides the questions and answers to an ACC/421 final exam. It contains 30 multiple choice questions testing concepts related to accounting, financial statements, ratios, and cash flow statements. The document encourages the reader to leave positive feedback if it helped them and wishes them good luck on their exam.
(New) final exam for mgt 521 all correct answers 100%
This document provides the questions and answers to a multiple choice exam on management and business topics. It covers areas like communication, marketing, leadership, ethics, entrepreneurship, accounting, economics, human resources, and strategic planning. The questions test understanding of key concepts in these areas and ability to identify examples and apply definitions. The document suggests the answers may not completely cover the actual exam being taken.
Criminología i concepto objeto de estudio y entidad científica6
Este documento discute el concepto, objeto de estudio y entidad científica de la criminología. Explica que la criminología surgió en el siglo XIX para estudiar el comportamiento antisocial siguiendo el método científico con el fin de prevenir el crimen. Aunque hay diferentes definiciones de criminología, se define generalmente como la ciencia que aplica el método empírico al estudio del comportamiento antisocial y las reacciones sociales. El objeto de estudio incluye las causas de la delincuencia, el delincuente
Designing a Moodle Template Based on Quality Matters Standards
Presented at Baltimore Moodle Moot on July 24, 2013
http://adlerschoolonline.info/MoodleMootPresentations/index.html
The interplay of personal preference and social influence in sharing networks...
The document summarizes research on understanding how personal preferences and social influence affect sharing behaviors on social networks. It presents three key findings:
1) Personal preferences dominate individuals' decisions about what content to share, with people more likely to share items they themselves like.
2) Less than 1% of peoples' actions can typically be attributed to social influence from friends, with influence being overestimated in prior work.
3) Modeling both personal preferences and social influence may lead to a better understanding of how content spreads on networks and help improve recommendation systems and diffusion models.
Focusing the Course Design Process on Alignment
This document provides guidance for developing online courses at Adler School of Professional Psychology. It outlines a four-part course design and build process that ensures alignment between course objectives, content, and assessments. The process involves determining course details, creating a detailed course plan with weekly learning aims and associated materials/activities, developing weekly presentations, and revising the course based on feedback. Resources like a syllabus template and online teaching plan are provided to support faculty through the process. The goal is to develop high-quality, competency-aligned online courses at Adler School. Contact information is provided for those with additional questions.
(New) final exam for qnt 561 all correct answers 100%
This document provides the questions and multiple choice answers to a QNT/561 Final Exam. It contains 30 multiple choice questions covering various operations management topics like process types, quality management, inventory management, project management, forecasting, and supply chain management. The document requests an "A" feedback rating and wishes the reader good luck on the exam.
Causal Inference, Reinforcement Learning, and Continuous Optimization
This talk, given at NUCL.AI in 2016, is about how to use causal inference to optimize running systems in production.
Using the verb have
This document provides a summary of a teacher resource on using "have" and "has" in English. It contains 25 slides with examples of how to use these verbs correctly with singular and plural subjects. The slides cover topics like using "have" and "has" with "I", "you", "we", and "they"; practicing with example sentences; and a review chapter with exercises on verbs like "am", "is", and "are". The document aims to teach English grammar concepts related to subject-verb agreement.
5 Characteristics of an Effective Recommendation
An effective recommendation should be constructive by offering solutions rather than just problems, specific in identifying both strengths and weaknesses, measurable so progress can be determined, sensitive by keeping comments positive and avoiding negativity, and balanced by pointing out both strengths and weaknesses.
5.3.5 causal inference in research
1. The document outlines steps for conducting causal inference research including defining a hypothesis, study design, drawing a directed acyclic graph (DAG) to identify confounders, analyzing data while controlling for confounders, and making scientific inferences.
2. An example is provided on estimating associations between deworming, hygienic latrines, finished floors, and soil-transmitted helminth infections using a cross-sectional survey design.
3. Results show the adjusted prevalence ratios for single and combined exposures on deworming and household characteristics on infection, including any additive interaction effects.
Implant Supported Over Denture By Dr pitzak
This document provides information about implant retained dentures. It discusses why patients may choose implant retained dentures and shows pictures of patients who could benefit from them. It then demonstrates the clinical process of placing two locator attachments on an existing denture, including selecting the correct abutment height, torquing and X-raying the abutments, adding spacers and processing the attachments into the denture. The document identifies the various parts used in the process and provides tips for techniques like adding a lingual relief hole. It also demonstrates how to select replacement males of different retention levels.
Dowhy: An end-to-end library for causal inference
In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they are valid, and to what extent. However, most libraries for causal inference focus only on the task of providing powerful statistical estimators. We describe DoWhy, an open-source Python library that is built with causal assumptions as its first-class citizens, based on the formal framework of causal graphs to specify and test causal assumptions. DoWhy presents an API for the four steps common to any causal analysis---1) modeling the data using a causal graph and structural assumptions, 2) identifying whether the desired effect is estimable under the causal model, 3) estimating the effect using statistical estimators, and finally 4) refuting the obtained estimate through robustness checks and sensitivity analyses. In particular, DoWhy implements a number of robustness checks including placebo tests, bootstrap tests, and tests for unoberved confounding. DoWhy is an extensible library that supports interoperability with other implementations, such as EconML and CausalML for the the estimation step.
Alleviating Privacy Attacks Using Causal Models
Machine learning models, especially deep neural networks have been shown to reveal membership information of inputs in the training data. Such membership inference attacks are a serious privacy concern, for example, patients providing medical records to build a model that detects HIV would not want their identity to be leaked. Further, we show that the attack accuracy amplifies when the model is used to predict samples that come from a different distribution than the training set, which is often the case in real world applications. Therefore, we propose the use of causal learning approaches where a model learns the causal relationship between the input features and the outcome. An ideal causal model is known to be invariant to the training distribution and hence generalizes well to shifts between samples from the same distribution and across different distributions. First, we prove that models learned using causal structure provide stronger differential privacy guarantees than associational models under reasonable assumptions. Next, we show that causal models trained on sufficiently large samples are robust to membership inference attacks across different distributions of datasets and those trained on smaller sample sizes always have lower attack accuracy than corresponding associational models. Finally, we confirm our theoretical claims with experimental evaluation on 4 moderately complex Bayesian network datasets and a colored MNIST image dataset. Associational models exhibit upto 80\% attack accuracy under different test distributions and sample sizes whereas causal models exhibit attack accuracy close to a random guess. Our results confirm the value of the generalizability of causal models in reducing susceptibility to privacy attacks. Paper available at https://arxiv.org/abs/1909.12732
DoWhy Python library for causal inference: An End-to-End tool
As computing systems are more frequently and more actively intervening in societally critical domains such as healthcare, education, and governance, it is critical to correctly predict and understand the causal effects of these interventions. Without an A/B test, conventional machine learning methods, built on pattern recognition and correlational analyses, are insufficient for causal reasoning.
Much like machine learning libraries have done for prediction, "DoWhy" is a Python library that aims to spark causal thinking and analysis. DoWhy provides a unified interface for causal inference methods and automatically tests many assumptions, thus making inference accessible to non-experts.
For a quick introduction to causal inference, check out amit-sharma/causal-inference-tutorial. We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (KDD 2018) conference: causalinference.gitlab.io/kdd-tutorial.
The Impact of Computing Systems | Causal inference in practice
This document discusses the need for causal inference techniques when evaluating the impact of computing systems on people's lives. It provides several examples where naively using predictive metrics or overall aggregated statistics can obscure important effects. Causal inference methods that consider context and attempt to isolate causal effects, such as through conditioning on relevant variables, are needed to properly understand how systems influence users and society.
Artificial Intelligence for Societal Impact
Artificial intelligence can help address societal problems in areas like healthcare, education, financial inclusion and more. The document discusses two examples: (1) Using machine learning to help tuberculosis health workers prioritize patients by predicting adherence and outcomes, and (2) Analyzing online mental health forums to understand effective peer support and identify "moments of change". While correlations can be misleading, focused work with domain experts can produce models that achieve accuracy over 70% in predicting moments of change. Overall, AI is an amplifier that must be developed responsibly with societal impact in mind.
Measuring effectiveness of machine learning systems
Many online systems, such as recommender systems or ad systems, are increasingly being used in societally critical domains such as education, healthcare, finance and governance. A natural question to ask is about their effectiveness, which is often measured using observational metrics. However, these metrics hide cause-and-effect processes between these systems, people's behavior and outcomes. I will present a causal framework that allows us to tackle questions about the effects of algorithmic systems and demonstrate its usage through evaluation of Amazon's recommender system and a major search engine. I will also discuss how such evaluations can lead to metrics for designing better systems.
Causal data mining: Identifying causal effects at scale
Identifying causal effects is an integral part of scientific inquiry, spanning a wide range of questions such as understanding behavior in online systems, effect of social policies, or risk factors for diseases. In the absence of a randomized experiment, however, traditional methods such as matching or instrumental variables fail to provide robust estimates because they depend on strong assumptions that are never tested.
My research shows that many of the strong assumptions are testable. This leads to a data mining framework for causal inference from observed data: instead of relying on untestable assumptions, we develop tests for valid experiment-like data---a "natural" experiment---and estimate causal effects only from subsets of data that pass those tests. Two such methods are presented. The first utilizes auxiliary data from large-scale systems to automate the search for natural experiments. Applying it to estimate the additional activity caused by Amazon's recommendation system, I find over 20,000 natural experiments, an order of magnitude more than those in past work. These experiments indicate that less than half of the click-throughs typically attributed to the recommendation system are causal; the rest would have happened anyways. The second method proposes a general Bayesian test that can be used for validating natural experiments in any dataset. For instance, I find that a majority of natural experiments used in recent studies in a premier economics journal are likely invalid. More generally, the proposed framework presents a viable way of doing causal inference in large-scale datasets with minimal assumptions.
Auditing search engines for differential satisfaction across demographics
This document presents a framework for auditing search engines to detect differences in user satisfaction across demographics. It describes three methods for more meaningful auditing that control for natural demographic variations: 1) Context matching to select near-identical user activity, 2) A hierarchical query-level model to borrow strength across popular queries, and 3) A query-level pairwise model to directly estimate relative satisfaction between user pairs for the same query. The framework found some light trends of older users being more satisfied but showed auditing is nuanced and different from measuring metrics on binned traffic alone. It provides a general approach for auditing systems using different metrics and user groups.
Equivalence causal frameworks: SEMs, Graphical models and Potential Outcomes
Causal inference finds applications in a variety of fields, ranging from the social to the biomedical sciences. Over the years, different fields have approached the problem separately and have developed varying methods and terminologies. On the surface, these look very different and proponents of these different languages of causal inference seem divided on their validity. However, at their heart, the key frameworks---structural equation modeling, causal graphical models, and potential outcomes---are all valid tools to solve the same problem and for most common applications, provide equivalent results. In this talk, I will review these three frameworks and provide high-level comparisons between them, hoping to resolve some of the debates around these different frameworks.
Estimating the causal impact of recommender systems
Presentation of ACM EC 2015 paper at the International Conference on Computational Social Science.
Link to paper: http://www.amitsharma.in/pubs/ec15_causal_impact_recommendations.pdf
Predictability of popularity on online social media: Gaps between prediction ...
Can we predict the future popularity of a song, movie or tweet? Recent work suggests that although it may be hard to predict an item’s popularity when it is first introduced, peeking into its early adopters and properties of their social network makes the problem easier. We test the robustness of such claims by using data from social networks spanning music, books, photos, and URLs. We find a stronger result: not only do predictive models with peeking achieve high accuracy on all datasets, they also generalize well, so much so that models trained on any one dataset perform with comparable accuracy on items from other datasets. Though practically useful, our models (and those in other work) are intellectually unsatisfying because common formulations of the problem, which involve peeking at the first small-k adopters and predicting whether items end up in the top half of popular items, are both too sensitive to the speed of early adoption and too easy. Most of the predictive power comes from looking at how quickly items reach their first few adopters, while for other features of early adopters and their networks, even the direction of correlation with popularity is not consistent across domains. Problem formulations that examine items that reach k adopters in about the same amount of time reduce the importance of temporal features,
but also overall accuracy, highlighting that we understand little about why items become popular while providing a context in which we might build that understanding.
Estimating influence of online activity feeds on people's actions
How much do activity feeds in online social networks influence our actions? Short of doing a randomized experiment, this is a very tricky question to answer. This is because it requires answering the counterfactual: "how would users' behavior have changed if there was no feed in a social networking website?"
We propose a data-driven matching procedure that is able to control for underlying homophily effects and estimate the effect of feeds on actions in multiple social networks, including Last.fm, Goodreads, Flickr and Flixster. We show that most actions in these social networks are not influenced by the feed at all, suggesting that influence from friends' feeds may be overrated.
Causal inference in practice: Here, there, causality is everywhere
Graphical models present a useful abstraction of causal pathways, but there are numerous causal pathways in the real world. It is often not clear whether a causal graph correctly models a given scenario, or when it does, whether we have enough data to estimate causal effects.
In such a messy scenario, it would not be a bad decision to abandon the search for causality altogether, if not for the far-reaching, important effects of causality in almost all branches of scientific inquiry.
Here are some examples:
• Economic Policy: How much does the school you go to affect your future earnings? How bad is military transcription for future earnings or wellbeing of an individual?
• Political science: How do televised ads from presidential candidates affect voter turnouts? Does mandating quotas for women in local government lead to changes in policy?
• Human behavior: Does public recognition of scholastic ability lead to better performance? If you friend smokes, would you smoke too?
• Biology and medicine: Does Vitamin-D deficiency increase chances of colorectal cancer? Are heart attack surgeries beneficial for future health of patients?
• Web applications: How much does the Facebook newsfeed influence the information we are exposed to? How much do recommendation systems shape our purchasing decisions?
In this talk, we will select a bunch of these as motivating examples and discuss the four main methodologies for estimating causal effects. All through, our goal will be make connections to bayesian networks/graphical models and show how they can serve as a unifying framework for dealing with causality.
Outline:
• Controlled randomized experiments
Conditioning on observable confounders (back door criterion)
-- Stratification
-- Matching
-- Regression(?)
• Mechanism-based strategies (Front door criterion)
• Natural experiments
-- As-if experiments
-- Instrumental variables
-- Regression discontinuities
More Related Content
Viewers also liked
Designing a Moodle Template Based on Quality Matters Standards
Presented at Baltimore Moodle Moot on July 24, 2013
http://adlerschoolonline.info/MoodleMootPresentations/index.html
The interplay of personal preference and social influence in sharing networks...
The document summarizes research on understanding how personal preferences and social influence affect sharing behaviors on social networks. It presents three key findings:
1) Personal preferences dominate individuals' decisions about what content to share, with people more likely to share items they themselves like.
2) Less than 1% of peoples' actions can typically be attributed to social influence from friends, with influence being overestimated in prior work.
3) Modeling both personal preferences and social influence may lead to a better understanding of how content spreads on networks and help improve recommendation systems and diffusion models.
Focusing the Course Design Process on Alignment
This document provides guidance for developing online courses at Adler School of Professional Psychology. It outlines a four-part course design and build process that ensures alignment between course objectives, content, and assessments. The process involves determining course details, creating a detailed course plan with weekly learning aims and associated materials/activities, developing weekly presentations, and revising the course based on feedback. Resources like a syllabus template and online teaching plan are provided to support faculty through the process. The goal is to develop high-quality, competency-aligned online courses at Adler School. Contact information is provided for those with additional questions.
(New) final exam for qnt 561 all correct answers 100%
This document provides the questions and multiple choice answers to a QNT/561 Final Exam. It contains 30 multiple choice questions covering various operations management topics like process types, quality management, inventory management, project management, forecasting, and supply chain management. The document requests an "A" feedback rating and wishes the reader good luck on the exam.
Causal Inference, Reinforcement Learning, and Continuous Optimization
This talk, given at NUCL.AI in 2016, is about how to use causal inference to optimize running systems in production.
Using the verb have
This document provides a summary of a teacher resource on using "have" and "has" in English. It contains 25 slides with examples of how to use these verbs correctly with singular and plural subjects. The slides cover topics like using "have" and "has" with "I", "you", "we", and "they"; practicing with example sentences; and a review chapter with exercises on verbs like "am", "is", and "are". The document aims to teach English grammar concepts related to subject-verb agreement.
5 Characteristics of an Effective Recommendation
An effective recommendation should be constructive by offering solutions rather than just problems, specific in identifying both strengths and weaknesses, measurable so progress can be determined, sensitive by keeping comments positive and avoiding negativity, and balanced by pointing out both strengths and weaknesses.
5.3.5 causal inference in research
1. The document outlines steps for conducting causal inference research including defining a hypothesis, study design, drawing a directed acyclic graph (DAG) to identify confounders, analyzing data while controlling for confounders, and making scientific inferences.
2. An example is provided on estimating associations between deworming, hygienic latrines, finished floors, and soil-transmitted helminth infections using a cross-sectional survey design.
3. Results show the adjusted prevalence ratios for single and combined exposures on deworming and household characteristics on infection, including any additive interaction effects.
Implant Supported Over Denture By Dr pitzak
This document provides information about implant retained dentures. It discusses why patients may choose implant retained dentures and shows pictures of patients who could benefit from them. It then demonstrates the clinical process of placing two locator attachments on an existing denture, including selecting the correct abutment height, torquing and X-raying the abutments, adding spacers and processing the attachments into the denture. The document identifies the various parts used in the process and provides tips for techniques like adding a lingual relief hole. It also demonstrates how to select replacement males of different retention levels.
Viewers also liked (9)
Designing a Moodle Template Based on Quality Matters Standards
Designing a Moodle Template Based on Quality Matters Standards
The interplay of personal preference and social influence in sharing networks...
The interplay of personal preference and social influence in sharing networks...
(New) final exam for qnt 561 all correct answers 100%
(New) final exam for qnt 561 all correct answers 100%
Causal Inference, Reinforcement Learning, and Continuous Optimization
Causal Inference, Reinforcement Learning, and Continuous Optimization
More from Amit Sharma
Dowhy: An end-to-end library for causal inference
In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they are valid, and to what extent. However, most libraries for causal inference focus only on the task of providing powerful statistical estimators. We describe DoWhy, an open-source Python library that is built with causal assumptions as its first-class citizens, based on the formal framework of causal graphs to specify and test causal assumptions. DoWhy presents an API for the four steps common to any causal analysis---1) modeling the data using a causal graph and structural assumptions, 2) identifying whether the desired effect is estimable under the causal model, 3) estimating the effect using statistical estimators, and finally 4) refuting the obtained estimate through robustness checks and sensitivity analyses. In particular, DoWhy implements a number of robustness checks including placebo tests, bootstrap tests, and tests for unoberved confounding. DoWhy is an extensible library that supports interoperability with other implementations, such as EconML and CausalML for the the estimation step.
Alleviating Privacy Attacks Using Causal Models
Machine learning models, especially deep neural networks have been shown to reveal membership information of inputs in the training data. Such membership inference attacks are a serious privacy concern, for example, patients providing medical records to build a model that detects HIV would not want their identity to be leaked. Further, we show that the attack accuracy amplifies when the model is used to predict samples that come from a different distribution than the training set, which is often the case in real world applications. Therefore, we propose the use of causal learning approaches where a model learns the causal relationship between the input features and the outcome. An ideal causal model is known to be invariant to the training distribution and hence generalizes well to shifts between samples from the same distribution and across different distributions. First, we prove that models learned using causal structure provide stronger differential privacy guarantees than associational models under reasonable assumptions. Next, we show that causal models trained on sufficiently large samples are robust to membership inference attacks across different distributions of datasets and those trained on smaller sample sizes always have lower attack accuracy than corresponding associational models. Finally, we confirm our theoretical claims with experimental evaluation on 4 moderately complex Bayesian network datasets and a colored MNIST image dataset. Associational models exhibit upto 80\% attack accuracy under different test distributions and sample sizes whereas causal models exhibit attack accuracy close to a random guess. Our results confirm the value of the generalizability of causal models in reducing susceptibility to privacy attacks. Paper available at https://arxiv.org/abs/1909.12732
DoWhy Python library for causal inference: An End-to-End tool
As computing systems are more frequently and more actively intervening in societally critical domains such as healthcare, education, and governance, it is critical to correctly predict and understand the causal effects of these interventions. Without an A/B test, conventional machine learning methods, built on pattern recognition and correlational analyses, are insufficient for causal reasoning.
Much like machine learning libraries have done for prediction, "DoWhy" is a Python library that aims to spark causal thinking and analysis. DoWhy provides a unified interface for causal inference methods and automatically tests many assumptions, thus making inference accessible to non-experts.
For a quick introduction to causal inference, check out amit-sharma/causal-inference-tutorial. We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (KDD 2018) conference: causalinference.gitlab.io/kdd-tutorial.
The Impact of Computing Systems | Causal inference in practice
This document discusses the need for causal inference techniques when evaluating the impact of computing systems on people's lives. It provides several examples where naively using predictive metrics or overall aggregated statistics can obscure important effects. Causal inference methods that consider context and attempt to isolate causal effects, such as through conditioning on relevant variables, are needed to properly understand how systems influence users and society.
Artificial Intelligence for Societal Impact
Artificial intelligence can help address societal problems in areas like healthcare, education, financial inclusion and more. The document discusses two examples: (1) Using machine learning to help tuberculosis health workers prioritize patients by predicting adherence and outcomes, and (2) Analyzing online mental health forums to understand effective peer support and identify "moments of change". While correlations can be misleading, focused work with domain experts can produce models that achieve accuracy over 70% in predicting moments of change. Overall, AI is an amplifier that must be developed responsibly with societal impact in mind.
Measuring effectiveness of machine learning systems
Many online systems, such as recommender systems or ad systems, are increasingly being used in societally critical domains such as education, healthcare, finance and governance. A natural question to ask is about their effectiveness, which is often measured using observational metrics. However, these metrics hide cause-and-effect processes between these systems, people's behavior and outcomes. I will present a causal framework that allows us to tackle questions about the effects of algorithmic systems and demonstrate its usage through evaluation of Amazon's recommender system and a major search engine. I will also discuss how such evaluations can lead to metrics for designing better systems.
Causal data mining: Identifying causal effects at scale
Identifying causal effects is an integral part of scientific inquiry, spanning a wide range of questions such as understanding behavior in online systems, effect of social policies, or risk factors for diseases. In the absence of a randomized experiment, however, traditional methods such as matching or instrumental variables fail to provide robust estimates because they depend on strong assumptions that are never tested.
My research shows that many of the strong assumptions are testable. This leads to a data mining framework for causal inference from observed data: instead of relying on untestable assumptions, we develop tests for valid experiment-like data---a "natural" experiment---and estimate causal effects only from subsets of data that pass those tests. Two such methods are presented. The first utilizes auxiliary data from large-scale systems to automate the search for natural experiments. Applying it to estimate the additional activity caused by Amazon's recommendation system, I find over 20,000 natural experiments, an order of magnitude more than those in past work. These experiments indicate that less than half of the click-throughs typically attributed to the recommendation system are causal; the rest would have happened anyways. The second method proposes a general Bayesian test that can be used for validating natural experiments in any dataset. For instance, I find that a majority of natural experiments used in recent studies in a premier economics journal are likely invalid. More generally, the proposed framework presents a viable way of doing causal inference in large-scale datasets with minimal assumptions.
Auditing search engines for differential satisfaction across demographics
This document presents a framework for auditing search engines to detect differences in user satisfaction across demographics. It describes three methods for more meaningful auditing that control for natural demographic variations: 1) Context matching to select near-identical user activity, 2) A hierarchical query-level model to borrow strength across popular queries, and 3) A query-level pairwise model to directly estimate relative satisfaction between user pairs for the same query. The framework found some light trends of older users being more satisfied but showed auditing is nuanced and different from measuring metrics on binned traffic alone. It provides a general approach for auditing systems using different metrics and user groups.
Equivalence causal frameworks: SEMs, Graphical models and Potential Outcomes
Causal inference finds applications in a variety of fields, ranging from the social to the biomedical sciences. Over the years, different fields have approached the problem separately and have developed varying methods and terminologies. On the surface, these look very different and proponents of these different languages of causal inference seem divided on their validity. However, at their heart, the key frameworks---structural equation modeling, causal graphical models, and potential outcomes---are all valid tools to solve the same problem and for most common applications, provide equivalent results. In this talk, I will review these three frameworks and provide high-level comparisons between them, hoping to resolve some of the debates around these different frameworks.
Estimating the causal impact of recommender systems
Presentation of ACM EC 2015 paper at the International Conference on Computational Social Science.
Link to paper: http://www.amitsharma.in/pubs/ec15_causal_impact_recommendations.pdf
Predictability of popularity on online social media: Gaps between prediction ...
Can we predict the future popularity of a song, movie or tweet? Recent work suggests that although it may be hard to predict an item’s popularity when it is first introduced, peeking into its early adopters and properties of their social network makes the problem easier. We test the robustness of such claims by using data from social networks spanning music, books, photos, and URLs. We find a stronger result: not only do predictive models with peeking achieve high accuracy on all datasets, they also generalize well, so much so that models trained on any one dataset perform with comparable accuracy on items from other datasets. Though practically useful, our models (and those in other work) are intellectually unsatisfying because common formulations of the problem, which involve peeking at the first small-k adopters and predicting whether items end up in the top half of popular items, are both too sensitive to the speed of early adoption and too easy. Most of the predictive power comes from looking at how quickly items reach their first few adopters, while for other features of early adopters and their networks, even the direction of correlation with popularity is not consistent across domains. Problem formulations that examine items that reach k adopters in about the same amount of time reduce the importance of temporal features,
but also overall accuracy, highlighting that we understand little about why items become popular while providing a context in which we might build that understanding.
Estimating influence of online activity feeds on people's actions
How much do activity feeds in online social networks influence our actions? Short of doing a randomized experiment, this is a very tricky question to answer. This is because it requires answering the counterfactual: "how would users' behavior have changed if there was no feed in a social networking website?"
We propose a data-driven matching procedure that is able to control for underlying homophily effects and estimate the effect of feeds on actions in multiple social networks, including Last.fm, Goodreads, Flickr and Flixster. We show that most actions in these social networks are not influenced by the feed at all, suggesting that influence from friends' feeds may be overrated.
Causal inference in practice: Here, there, causality is everywhere
Graphical models present a useful abstraction of causal pathways, but there are numerous causal pathways in the real world. It is often not clear whether a causal graph correctly models a given scenario, or when it does, whether we have enough data to estimate causal effects.
In such a messy scenario, it would not be a bad decision to abandon the search for causality altogether, if not for the far-reaching, important effects of causality in almost all branches of scientific inquiry.
Here are some examples:
• Economic Policy: How much does the school you go to affect your future earnings? How bad is military transcription for future earnings or wellbeing of an individual?
• Political science: How do televised ads from presidential candidates affect voter turnouts? Does mandating quotas for women in local government lead to changes in policy?
• Human behavior: Does public recognition of scholastic ability lead to better performance? If you friend smokes, would you smoke too?
• Biology and medicine: Does Vitamin-D deficiency increase chances of colorectal cancer? Are heart attack surgeries beneficial for future health of patients?
• Web applications: How much does the Facebook newsfeed influence the information we are exposed to? How much do recommendation systems shape our purchasing decisions?
In this talk, we will select a bunch of these as motivating examples and discuss the four main methodologies for estimating causal effects. All through, our goal will be make connections to bayesian networks/graphical models and show how they can serve as a unifying framework for dealing with causality.
Outline:
• Controlled randomized experiments
Conditioning on observable confounders (back door criterion)
-- Stratification
-- Matching
-- Regression(?)
• Mechanism-based strategies (Front door criterion)
• Natural experiments
-- As-if experiments
-- Instrumental variables
-- Regression discontinuities
The role of social connections in shaping our preferences
The document discusses social influence and how social connections shape preferences. It examines three questions: whether friends' activities can predict our own, if friends' activities can help with decisions and opinions, and if friends would suggest items we like. The author has conducted multiple studies on social networks like Facebook and Twitter to analyze how preferences cluster among friends and how social explanations can influence recommendations. Their findings show preferences tend to be localized among friends and certain social explanations like close friends are more influential than popularity-based explanations.
[RecSys '13]Pairwise Learning: Experiments with Community Recommendation on L...
1) The document proposes pairwise learning models for community recommendations on LinkedIn that learn preferences between communities rather than individual recommendations.
2) Three pairwise models are introduced - a feature difference model, logistic loss model, and pairwise PLSI latent preference model.
3) Evaluation on LinkedIn data shows the pairwise PLSI model improves performance on learning pairwise preferences and leads to more successful recommendations compared to baseline models. Online testing also showed click-through-rate increases of 3-5% for the pairwise models over baseline methods.
RSWEB 2013: A research platform for social recommendation
This document summarizes a research platform called PopCore for conducting experiments on social recommendation. PopCore is based on Facebook data from over 500 core users and allows testing recommendation algorithms and hypotheses on a networked group of people. Previous experiments on PopCore have found that recommendations from a user's first-degree social connections are nearly as good as those from the whole network, and that an item's popularity within a user's ego network is more indicative of their preference than overall popularity. The document proposes a new study on PopCore to compare algorithmic recommendations to people-to-people suggestions.
More from Amit Sharma (16)
DoWhy Python library for causal inference: An End-to-End tool
DoWhy Python library for causal inference: An End-to-End tool
The Impact of Computing Systems | Causal inference in practice
The Impact of Computing Systems | Causal inference in practice
Measuring effectiveness of machine learning systems
Measuring effectiveness of machine learning systems
Causal data mining: Identifying causal effects at scale
Causal data mining: Identifying causal effects at scale
Auditing search engines for differential satisfaction across demographics
Auditing search engines for differential satisfaction across demographics
Equivalence causal frameworks: SEMs, Graphical models and Potential Outcomes
Equivalence causal frameworks: SEMs, Graphical models and Potential Outcomes
Estimating the causal impact of recommender systems
Estimating the causal impact of recommender systems
Predictability of popularity on online social media: Gaps between prediction ...
Predictability of popularity on online social media: Gaps between prediction ...
Estimating influence of online activity feeds on people's actions
Estimating influence of online activity feeds on people's actions
Causal inference in practice: Here, there, causality is everywhere
Causal inference in practice: Here, there, causality is everywhere
The role of social connections in shaping our preferences
The role of social connections in shaping our preferences
[RecSys '13]Pairwise Learning: Experiments with Community Recommendation on L...
[RecSys '13]Pairwise Learning: Experiments with Community Recommendation on L...
RSWEB 2013: A research platform for social recommendation
RSWEB 2013: A research platform for social recommendation
Recently uploaded
The Ipsos - AI - Monitor 2024 Report.pdf
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
毕业原版【微信:176555708】【(UMN毕业证书)明尼苏达大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
一比一原版巴斯大学毕业证(Bath毕业证书)学历如何办理
原版办理【微信号:BYZS866】【巴斯大学毕业证(Bath毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【关于学历材料质量】
我们承诺采用的是学校原版纸张（原版纸质、底色、纹路、）我们工厂拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有成品以及工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Experts live - Improving user adoption with AI
Bekijk de slides van onze sessie Enhancing Modern Workplace Efficiency op Experts Live 2024.
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"
Embark on a captivating financial journey with 'Financial Odyssey,' our hackathon project. Delve deep into the past performance of two companies as we employ an array of financial statement analysis techniques. From ratio analysis to trend analysis, uncover insights crucial for informed decision-making in the dynamic world of finance."
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
一比一原版(UO毕业证)渥太华大学毕业证如何办理
UO毕业证录取书【微信95270640】购买（渥太华大学毕业证成绩单硕士学历）Q微信95270640代办UO学历认证留信网伪造渥太华大学学位证书精仿渥太华大学本科/硕士文凭证书补办渥太华大学 diplomaoffer,Transcript购买渥太华大学毕业证成绩单购买UO假毕业证学位证书购买伪造渥太华大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
文凭办理流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：微信95270640我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
7完成交易删除客户资料
高精端提供以下服务：
一：渥太华大学渥太华大学毕业证文凭证书全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信95270640）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时多
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Global Situational Awareness of A.I. and where its headed
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
by
Timothy Spann
Principal Developer Advocate
https://budapestdata.hu/2024/en/
https://budapestml.hu/2024/en/
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://www.youtube.com/@flank-stack
milvus
vector database
gen ai
generative ai
deep learning
machine learning
apache nifi
apache pulsar
apache kafka
apache flink
Analysis insight about a Flyball dog competition team's performance
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
原版制作【微信:41543339】【利兹贝克特大学毕业证(LeedsBeckett毕业证书)】【微信:41543339】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Open Source Contributions to Postgres: The Basics POSETTE 2024
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
DSSML24_tspann_CodelessGenerativeAIPipelines
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
一比一原版(Unimelb毕业证书)墨尔本大学毕业证如何办理
原版制作【微信:41543339】【(Unimelb毕业证书)墨尔本大学毕业证】【微信:41543339】《成绩单、外壳、雅思、offer、留信学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同进口机器一比一制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Learn SQL from basic queries to Advance queries
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
毕业原版【微信:176555708】【(UCSF毕业证书)旧金山分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Recently uploaded (20)
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Global Situational Awareness of A.I. and where its headed
Global Situational Awareness of A.I. and where its headed
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
Analysis insight about a Flyball dog competition team's performance
Analysis insight about a Flyball dog competition team's performance
Open Source Contributions to Postgres: The Basics POSETTE 2024
Open Source Contributions to Postgres: The Basics POSETTE 2024
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
From prediction to causation: Causal inference in online systems
- 2. 2
- 3. 3
- 4. 4
- 5. 5
- 6. Use these correlations to make a predictive model. Future Activity -> f(number of friends, logins in past month) 6
- 7. 7
- 8. 8
- 9. 9
- 10. 10
- 11. 11
- 12. 12
- 13. 13
- 14. 14
- 15. 15
- 16. 16
- 17. 17
- 18. 18
- 19. 19 Old Algorithm (A) New Algorithm (B) 50/1000 (5%) 54/1000 (5.4%)
- 20. 20 Old Algorithm (A) New Algorithm (B) 10/400 (2.5%) 4/200 (2%) Old Algorithm (A) New Algorithm (B) 40/600 (6.6%) 50/800 (6.2%) 0 2 4 6 8 Low-activity High-activity CTR
- 21. Is Algorithm A better? Old algorithm (A) New Algorithm (B) CTR for Low- Activity users 10/400 (2.5%) 4/200 (2%) CTR for High- Activity users 40/600 (6.6%) 50/800 (6.2%) Total CTR 50/1000 (5%) 54/1000 (5.4%) 21
- 22. 22
- 23. Average comment length decreases over time. 23 But for each yearly cohort of users, comment length increases over time.
- 24. 24
- 25. 25
- 26. 26
- 29. 29
- 30. 30
- 31. 31
- 32. 32
- 33. 33
- 34. 34
- 35. 35
- 36. 36
- 37. 37
- 38. 38
- 39. 39
- 40. 40
- 41. 41Dunning (2002), Rosenzweig-Wolpin (2000)
- 42. 42
- 43. 43
- 44. 44
- 45. 45
- 46. 46
- 47. 47
- 48. 48
- 49. 49
- 50. 50
- 51. 51
- 52. 52
- 53. 53
- 54. 54
- 55. 55 Does new Algorithm B increase CTR for recommendations on Windows Store, compared to old algorithm A?
- 56. Does new Algorithm B increase CTR for recommendations on Windows Store, compared to old algorithm A? 56
- 57. 57
- 58. 58
- 59. 59
- 60. 60
- 61. 61
- 62. 62
- 63. 63
- 64. 64
- 65. 65
- 66. 𝑷𝒓𝒐𝒑𝒆𝒏𝒔𝒊𝒕𝒚 𝑁𝑒𝑤𝐴𝑙𝑔𝑜 𝑈𝑠𝑒𝑟𝑖 = 𝑳𝒐𝒈𝒊𝒔𝒕𝒊𝒄(𝑎 𝑐𝑎𝑡1, 𝑎 𝑐𝑎𝑡2, … 𝑎 𝑐𝑎𝑡𝑛) Compare CTR between users with the same propensity score. 66
- 67. 67
- 68. 68
- 70. 70
- 71. 71
- 72. 72
- 74. 74
- 77. 77
- 78. 78
- 79. 79
- 80. 80
- 81. 81
- 82. > nrow(user_app_visits_A) [1] 1,000,000 > length(unique(user_app_visits_A$user_id)) [1] 10,000 > length(unique(user_app_visits_A$product_id)) [1] 990 > length(unique(user_app_visits_A$category)) [1] 10 82
- 83. 83
- 84. 84
- 85. > user_app_visits_B = read.csv("user_app_visits_B.csv") > naive_observational_estimate <- function(user_visits){ # Naive observational estimate # Simply the fraction of visits that resulted in a recommendation click- through. est = summarise(user_visits, naive_estimate=sum(is_rec_visit)/length(is_rec_visit)) return(est) } > naive_observational_estimate(user_app_visits_A) naive_estimate [1] 0.200768 > naive_observational_estimate(user_app_visits_B) naive_estimate [1] 0.226467 85
- 86. 86
- 87. > stratified_by_activity_estimate(user_app_visits_A) Source: local data frame [4 x 2] activity_level stratified_estimate 1 1 0.1248852 2 2 0.1750483 3 3 0.2266394 4 4 0.2763522 > stratified_by_activity_estimate(user_app_visits_B) Source: local data frame [4 x 2] activity_level stratified_estimate 1 1 0.1253469 2 2 0.1753933 3 3 0.2257211 4 4 0.2749867 87
- 88. > stratified_by_category_estimate(user_app_visits_A) Source: local data frame [10 x 2] category stratified_estimate 1 1 0.1758294 2 2 0.2276829 3 3 0.2763157 4 4 0.1239860 5 5 0.1767163 … … … > stratified_by_category_estimate(user_app_visits_B) Source: local data frame [10 x 2] category stratified_estimate 1 1 0.2002127 2 2 0.2517528 3 3 0.3021371 4 4 0.1503150 5 5 0.1999519 … … … 88
- 89. 89
- 90. 90
- 91. 91
- 92. 92
- 93. > naive_observational_estimate(user_app_visits_A) naive_estimate [1] 0.200768 > ranking_discontinuity_estimate(user_app_visits_A) discontinuity_estimate [1] 0.121362 40% of app visits coming from recommendation click- throughs are not causal. Could have happened even without the recommendation system. 93
- 94. 94