The document discusses the importance of causality across various fields including economics, political science, human behavior, and biology. It outlines methods for estimating causal effects, emphasizing the use of graphical models and the distinction between randomized experiments and observational data. The author highlights the practical implications of understanding causality in recommender systems and social networks.

1. Here, there,
causality is
everywhere
Amit Sharma
Microsoft Research, New York

2. My route to causality
Building
recommender
systems in social
Networks
Conducting user
experiments
Estimating impact of
recommendations
and social feeds

3. Causality is everywhere
Spans every branch of science.
Aristotle: to know, is to know the final cause.
Two approaches:
Randomized experiments (Fisher): Gold standard
Observational data: Messy.

4. Outline
Causality is everywhere
Economics
Political Science
Human Behavior
Biology and Medicine
Online systems
Estimating causality using graphical models
Conditioning
Mechanism-based
Natural Experiments
The promise of graphical models

5. Causality in economics
David Card. The causal effect of education on earnings (1999)
Conley and Heerwig. The Long-Term Effects of Military
Conscription on Mortality: Estimates From the Vietnam-Era Draft
Lottery (2012)

6. Causality in political science
Darrell West. Air Wars (2013)
Chattopadhyay and Duflo. Women as Policy Makers:
Evidence from a Randomized Policy Experiment in
India (2004)

7. Causality in human behavior
Thistlewaithe and Campbell. Effect of public recognition
of scholastic achievement (1960)
Christakis and Fowler. The collective dynamics of
smoking in a large social network (2008)

8. Causality in biology and medicine
Effect of Vitamin D deficiency on colon cancer
Effect of heart attack surgery on long-term
health of patient

9. Causality in web applications
Sharma and Cosley. Distinguishing between personal preference
and homophily in online activity feeds (2016).
Sharma, Hofman and Watts. Estimating the causal impact of
recommender systems (2015).

10. Methods for estimating causal
effects from observational data
Condition on observed
covariates
• Stratification
• Matching
• Regression (?)
Mechanism-based
strategies
• Path-based
approaches
Natural experiments
• As-if experiments
• Instrumental
Variables
• Regression
discontinuity

11. Towards unifying estimation
strategies: Causal graphical models

12. I. Ideal: Randomized experiments

13. II. Conditioning on observed
covariates
Corresponds to Backdoor criterion.

14. a) Stratification
Condition on different levels of socio-
economic status.

15. b) Matching
Socio-Economic status is a function of parents’
income, locality and other observed indicators.

16. b) Matching
Model propensity to attend a particular school.
Pschool = f(PI, Loc, …)

17. c) Regression
Condition on observed covariates by
adding them as independent variables in
regression.
Works only if true causal
relationship between
variables is linear.

18. III. Mechanism-based strategies
Corresponds to Front door criterion.

19. IV. Natural Experiments
Look for experiments happening in the real world.
Promise greater generalizability than controlled lab
experiments.
Require greater care to ensure validity of causal identification.

20. a. (As-if) random experiments

21. b) Instrumental variables
Shock!
Increase in
traffic

22. c) Regression discontinuity

23. The promise of graphical models
Which variables to condition on?

24. Two graphical criteria explain all of
conventional approaches
A principled, succinct framework for causality.
Allows arbitrary functional forms for relationships between
variables.
Leads to clear statements about causal assumptions.
If a causal effect can be identified, it can be derived using do-
calculus (helpful for bigger graphs).

25. Graphical models form a succinct,
consistent and complete framework
for causality.
They are also practical.
thank you!
Amit Sharma
http://www.amitsharma.in