The document discusses causal inference and its importance for business decision making. It notes that causal inference allows companies to make better decisions by understanding the causal effects of actions using past data. This can help companies optimize outcomes by targeting interventions only to individuals expected to benefit. The document outlines key causal inference concepts like treatment effects and explores methods like matching, weighting, and experiments to estimate causal relationships from data.

1. Business Optimization via
Causal Inference
HayaData 2021
Outcome
Action

2. Why Causal Inference?
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3. The Big Three Questions
1. What happened?
2. What will happen?
3. How can I make it happen?
Griffin, D. K. (2020). The Big Three: A Methodology to Increase Data Science ROI by Answering the Questions Companies Care About. arXiv preprint arXiv:2002.07069.
Athey, S. (2017). Beyond prediction: Using big data for policy problems. Science, 355(6324), 483-485.
Bertsimas, D., & Kallus, N. (2020). From predictive to prescriptive analytics. Management Science, 66(3), 1025-1044.

4. Motivation: Better Decisions
§ Causal Inference allows you to make better decisions using past
experimental or observational data (+assumptions).
Data
Causal
Inference
Better
Decisions

5. Nobel Prize in Economics, October 2021
§"for their methodological
contributions to the analysis
of causal relationships."
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https://www.nobelprize.org/prizes/economic-sciences/2021/summary/

6. https://www.linkedin.com/company/vianai/jobs/
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7. Making Better Decision has Business Value
https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/most-of-ais-business-uses-will-be-in-two-areas

8. “Industry experts agree, the importance of causal data
science for data-augmented business decisions will only
grow in the future“
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https://causalscience.org/blog/causal-data-science-in-large-us-firms

9. Causal Inference in the Industry
“We analyze marketing campaigns and the impact of app preloads
using a fourth type of observational study format."
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"Causal Inference helps us provide a better user
experience for customers on the Uber platform"
“We rely on quasi-experiments and Causal Inference
methods, especially to measure new marketing and advertising ideas."

10. Why not just use Prediction?
Predicting churn and preventing churn
are not the same thing
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C
A
L
L
E
V
E
R
Y
O
N
E
P>80%
Predicting Churn
Then Target

11. Lost Causes
Prescriptive is Better
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Sure Things
Persuadables
Sleeping Dogs
C
A
L
L
D
O
N
’
T
C
A
L
L
D
O
N
’
T
C
A
L
L
D
O
N
’
T
C
A
L
L
Preventing Churn
Understanding the difference worth
300% better churn prevention
"Retention futility: Targeting high-risk customers might be ineffective." Ascarza, Eva. Journal of Marketing Research 55.1 (2018): 80-98.

12. Example: Who to Target (Uplift Modeling)
“Persuadables”
“Lost
causes”
“Sure
things”
“Sleeping dogs”
Treated Population
Cumulative
Effect
Treat
All
Treat
None
Optimal
Policy
Random Policy
0% 100%
§ Optimal Policy whould be to target only the population with positive
indivual treatment effect
§ 𝝅opt 𝒙 = '
𝑻 = 𝟏 if 𝝉 𝒙 > 𝟎
𝑻 = 𝟎 otherwise
Sorted by Effect 𝜏

13. Real Life Example: Uplift with a Design Partner
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§ “Should I target a customer ”?
§ Observational Analysis à
§ Experimentally Validated (30% uplift)
§ Call for design partners

14. Resources for Prescription
Controlled
Experiments
• Stats
Simulations
• RL
Observational
Study
• Causal

15. What is the Fundamental Problem?
§ Counterfactual is a missing data problem
§ Play make belief with potential outcomes
https://www.bradyneal.com/causal-inference-course
𝒊 𝑻 𝒀 𝒀𝟏
𝒀𝟎
𝝉 = 𝒀𝟏
− 𝒀𝟎
1 0 0 ? 0 ?
2 1 1 1 ? ?
3 1 0 0 ? ?
4 0 1 ? 1 ?
5 0 1 ? 1 ?
treatment outcome potential outcomes Individual treatment effect

16. Blake, T., Nosko, C. and Tadelis, S., 2015. Consumer heterogeneity and paid search effectiveness: A large-scale field experiment. Econometrica, 83(1), pp.155-174.
0 paid search $ revenue
$ paid search $ revenue
Slightly lower
WARNING: Misleading Attribution
Misconception… …Actual
“We are making
$4 for each $1
we spend”
“It’s $0.37…
We are actually
losing money”
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17. Warning: Bias
§ It is known that Exercise reduces Cholesterol
§ Yet, below is a scatter plot of the data
§ What can explain this?
Y
T
Y
T

18. Confounders Create Bias in Effect Estimation
§ Age is a confounder which effects both the treatment (Exercise) and the
outcome (Cholesterol)
§ We need to control for it!
https://towardsdatascience.com/implementing-causal-inference-a-key-step-towards-agi-de2cde8ea599
Y
T
X
Y
T
X
T
Y

19. Prescriptive is Neglected
§ Prescriptive methods seem to
be neglected
§ What is the effect of doing an
action A?
§ What is the optimal policy π to
maximize the KPI(s)?
https://www.kaggle.com/kaggle-survey-2020

20. How? Causal Inference 101
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21. Main Concepts
Causal DAG
𝑇 → 𝑌
Causal Discovery
𝑇→
?
𝑌
Potential Outcomes
𝑌!"#
= 𝑌 𝑑𝑜 𝑇 = 𝑡
ATE
𝜏$!% = 𝐸(𝑌&
) − 𝐸(𝑌'
)
CATE/ITE/THE
𝜏()*(X) = 𝐸 𝑌&
𝑋 − 𝐸(𝑌'
|𝑋)
Policy Evaluation
𝜋 𝑋 = Pr 𝑇 𝑋 ?
PE = 𝐸!~, - (𝑌)
Policy Optimization
PO = argmax
,
𝐸!~, - (𝑌)

22. Summary – Supervised vs Causal Learning
Supervised Learning Causal Inference
Predicts outcome 𝑃(𝑌|𝑋) effect of change 𝑃(𝑌|𝑑𝑜 𝑋)
Assumption Passive observer Decision maker
Train-Test Equailly distributed Distribution shift
Validation Easy, via hold-out Fundamental challenge.
Better prediction is NOT better
causal estimation
Feature set Quantitative (over fit / under fit) Qualitative – could cause a bias in
the estimate
Domain
Knoweledge
Nice to have, deep neural network
are doing beyond humans without
Essential to make assumptions to
avoid pitfalls

23. Typical Stages in a Causal Project
1. Causal Model
2. Identify
3. Estimate
4. *Evaluate & Optimize
5. Refute

24. Identifiability
§The ability to estimate causal effect from observed data.
§Stable Unit Treatment Value Assumption (SUTVA)
for 𝑖 ≠ 𝑗: 𝐴( ⊥ 𝐴) and 𝑌( ⊥ 𝐴)
§Consistency
𝐴 = 𝑎 → 𝑌 = 𝑌. ∀𝑎
§Ignorability
𝑌', 𝑌& ⊥ 𝐴|𝑋
§Positivity
𝑃 𝐴 = 𝑎 𝑋 = 𝑥 > 0 ∀𝑎, 𝑥
Paper accepted to CDSM21

25. Quiz: which assumption is this?

26. Estimation Methods
1. Stratification – aggregate over stratas
𝐸 𝑌/
= ∑𝑃 𝑋 𝐸(𝑌|𝑇 = 𝑡, 𝑋 = 𝑥)
Why is it correct?
𝐸 𝑌 𝑇 = 𝑡, 𝑋 = 𝑥
=
consisitency
𝐸 𝑌/
𝑇 = 𝑡, 𝑋 = 𝑥
=
ignorability
𝐸(𝑌/
|𝑋 = 𝑥)

27. Estimation Methods
1. Stratification – aggregate over stratas
2. Matching – find “twins” in high dim

28. Estimation Methods
1. Stratification – aggregate over stratas
2. Matching – find “twins” in high dimension
3. Propensity Matching – find twins in one dimension

29. Propensity Score
§ Define 𝐴 = 1 for treatment and 𝐴 = 0 for control, we will denote the
propensity score for subject 𝑖 by
𝜋0 = Pr(𝐴 = 1|𝑋0)
§ propensity is a “balancing score”: meaning if we control/match for it, we
will get unbiased effect estimation
𝑃 𝑋 𝜋 𝑋 = 𝑝, 𝐴 = 1 = 𝑃 𝑋 𝜋 𝑋 = 𝑝, 𝐴 = 0

30. Estimation Methods
1. Stratification – aggregate over stratas
2. Matching – find “twins” in high dimension
3. Propensity Matching – find twins in one dimension
4. IPTW - Inverse Propensity Treatment Weighting
Note: It can be shown that IPTW and standardization are equivalent
(Technical Point 2.3, see Appendix)

31. Inverse Propensity Weighting
§ 𝜋0 = Pr(𝐴0|𝑋 = 𝑥0)
§ 𝐴𝑇𝐸 = 𝐸 𝑌1
− 𝑌2
= ∑𝑌0
3!45!
5! 145!

32. What? In Practice
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33. Causal Python Open Sources
Microsoft/EconML

34. Causal Open Sources
EconML 1,700 Python
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Hünermund, P., Kaminski, J., & Schmitt, C. (2021). Causal Machine Learning and Business-Decision Making.

35. Causal Inference is Hard but Worth it
§ Hard
§ High entrance barrier - you can easily do it wrong
§ Validation and evaluation is hard
§ Domain Knowledge is (sometimes) essential
§ Valuable
§ Can Optimize decision making (“increamentality”)
§ Detect real effects and attribution
§ Personaliztion

36. Code for Toy Problem
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37. hanan@vian.ai
office hours 15:00-15:30
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38. P.J. Bickel, E.A. Hammel and J.W. O'Connell (1975). "Sex Bias in Graduate
Admissions: Data From Berkeley" (PDF). Science. 187 (4175): 398–404.

43. IPTW