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Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from...

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Causal Inference SLIDES BY: YI XIE, Nathan Yan, JIANNAN WANG 1
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Page 1: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Causal InferenceSLIDES BY:YI XIE, Nathan Yan, JIANNAN WANG

1

Page 2: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Outline

2

Why Should Data Scientists Care?

Basic Concepts

Causal Inference

Page 3: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Questions Data Scientists Can Answer

3

Is This A or B? ClassificationHow much or How Many? RegressionIs This Weird? Anomaly Detection

How Is This Organized? ClusteringWhat if? Causal Inference

Page 4: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

From Prediction to Causation

Predicting user activity for Xbox● Y = logins in next month● X = logins in past month, number of friends,...

What if we increase the number of friends?● Would it increase user activity?

4

Page 5: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Maybe or May be not (!)

A causes B

B causes A

C causes A and B

5

Number of Friends

Xbox Activity

Xbox Activity

Number of Friends

User interest in gaming

Number of Friends

Xbox Activity

Page 6: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

A/B Testing Helps!Treatment Group● A random sample of users● Launch a campaign to increase friends● Average activity next month

Control Group● A random sample of users● Not Launch a campaign to increase friends● Average activity next month

6

HypothesisTesting

Page 7: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

A/B Testing Does Not Work

● It is infeasible to do A/B testing○ What if you went to UBC rather than SFU,

would it help you land a better job?

● It is unethical to do A/B testing○ What if subscription price is set to $69 rather

than $99, would it increase revenue?

7

Page 8: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Example 1: Causality ≠ Correlation

8

When a team was investigating the city death rate data, theyfound that reported cases of teenage drowning death increasewhile the sales of ice cream also increase.

Page 9: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Example 1: Causality ≠ Correlation

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Page 10: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Example 2: Causality ≠ Correlation

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Is Berkeley gender biased?

Simpson's paradox

Page 11: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Example 2: Causality ≠ Correlation

11

Department

Admit RateGender

Page 12: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Example 3: Causality ≠ Correlation

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● In city areas with nearby trees andnatural landscapes, there is lessdomestic violence.

● Apartment complexes with manytrees had 52% fewer crimes.

● On tree-lined streets, people drivemore slowly, reducing accident risk.

Page 13: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Outline

13

Why Should Data Scientists Care?Basic Concepts● Outcome / Treatment Variables● Intervention and Do Operation● Counterfactual● Causal Graph

Causal Inference

Page 14: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Outcome / Treatment Variables

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Student Gender Class Study Program Grade

Jacky male 1 0 78

Terry male 1 1 82

Mary female 1 0 86

Sarah female 2 1 83

What if participating Study Program, would it improve Grade?

Treatment Outcome

Page 15: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Intervention and Do Operation❖ Do operator will signal the experimental

intervention (invented by Judea Pearl)

represents the distribution of grad if the person is enrolled in study program

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P(grade|do(enrolled in study program))

Page 16: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Intervention and Do Operation❖ P(A | B = b): probability of A being true given

that B is observed as B = b

❖ P(A | do(B) = b): probability of A being true given an intervention that sets B to b

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Page 17: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Counterfactual❖ What would have happened if I had changed

“Treatment Variable”

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Student Gender Class Study Program Actual Grade

Jacky male 1 0 78

Terry male 1 1 82

Mary female 1 0 86

Sarah female 2 1 83

82

82

90

85

(You cannot get it in reality)Counterfactual Grade

Page 18: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Causality

Causality Definition● The difference between actual outcome and

counterfactual outcome

The Fundamental Problem of Causal Inference● We cannot observe the counterfactual outcome

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Page 19: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Causal Graph

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Causal graph is a directed graph● Nodes: variables● Directed Edges: X affects Y

Page 20: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Causal Graph

20

❖ Confounding variables: common cause of treatment and outcome

GradeStudy Program

Unobserved Confounders (e.g., study habits, grade history)

Why Causal Graph?● Helpful to identify which

variables to control for● Make assumptions explicit

Page 21: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Outline

21

Why Should Data Scientists Care?Basic ConceptsCausal Inference● Statistical Inference vs. Causal Inference● Average Treatment Effect (ATE) Estimation ● Causal Inference in Practice

Page 22: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Statistical vs. Causal Inferences

Statistical inference ● Data is just a sample● Your goal is to infer a population● Think about how to go “backwards” from sample to population

Causal inference ● Derive a treatment group from Data ● Derive a control group (i.e., without treatment) from Data● Think about how to infer the actual effect of treatment from the

derived treatment and control groups

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Page 23: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Outline

23

Why Should Data Scientists Care?Basic ConceptsCausal Inference● Statistical Inference vs. Causal Inference● Average Treatment Effect (ATE) Estimation● Causal Inference in Practice

Page 24: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Individual Treatment Effect

What is the grade difference between enrolling and not enrolling in the study program?

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Student Gender Class Enroll in Study Program

Not Enroll in Study Program

Jacky male 1 82 78

Terry male 1 82 82

Mary female 1 90 86

Sarah female 2 83 85

4

0

4

-2

Page 25: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Average Treatment Effect (ATE)

The average of all values for individual treatment effects

25

Student Gender Class Enroll in Study Program

Not Enroll in Study Program

Jacky male 1 82 78

Terry male 1 82 82

Mary female 1 90 86

Sarah female 2 83 85

4

0

4

-2

ATE = (4 + 0 + 4 + -2) / 4 = 1.5

Page 26: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

ATE Estimation

ATE estimation methods❖ Matching based:

➢ Perfect matching➢ Nearest neighbor matching ➢ Propensity score matching

❖ ML based:➢ Regression method➢ Representation learning

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Page 27: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Perfect Matching

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student gender class Study program grade

Terry male 1 1 82

Sarah female 2 1 83

Jacky male 1 0 78

Mary female 1 0 86

Find the “perfect matching in counterfactual world”

Page 28: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Perfect Matching

28

student gender class Study program grade

Terry male 1 1 82

Sarah female 2 1 83

Jacky male 1 0 78

Mary female 1 0 86

Find the “perfect matching in counterfactual world”

4

Page 29: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Perfect Matching

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Student Gender Class Study Program Grade

Terry male 1 1 82

Sarah female 2 1 83

Jacky male 1 0 78

Mary female 1 0 86

By perfect matching, we can’t find ATE over the population, because for tuple Sarah, we can’t find the perfect match in control group

Page 30: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Nearest Neighbor Matching

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Find the “nearest matching in counterfactual world”

Student Gender Class Study Program Grade

Terry male 1 1 82

Sarah female 2 1 83

Jacky male 1 0 78

Mary female 1 0 86

3

Page 31: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Nearest Neighbor Matching

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ATE = ½ [3 + 4] = 3.5

4

3

Student Gender Class Study Program Grade

Terry male 1 1 82

Sarah female 2 1 83

Jacky male 1 0 78

Mary female 1 0 86

Page 32: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Propensity Score Matching

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Steps❖ Using logistic regression to infer 𝑒(𝑥)=𝑃𝑟[𝑇=1|𝑋=𝑥]

❖ Match users with treatment 0 with users with treatment 1 based on propensity score, using matching methods

Page 33: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Propensity Score Matching

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Based on PSM derived by logistic regression, the match we’ll find for Terry is Jacky, and the match we’ll find for Sarah is Mary

ATE = 3.5, computation is similar with nearest neighbor matching

Student Gender Class Study Program Grade

Terry male 1 1 82

Sarah female 2 1 83

Jacky male 1 0 78

Mary female 1 0 86

PSE

0.7

0.6

0.7

0.55

Page 34: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Regression Method

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Intuition: the distribution of Y given X is different when treatment is different

Train two separate regression models under Treatment = 0 or Treatment = 1, infer p(Y | T = 0, X) and p(Y | T = 1, X)

Page 35: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Regression Method

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Treat one regression model 1 on [Terry, Sarah] where Study Program = 1

treat another regression model 2 on [Jacky, Mary] where Study Program = 0

Using model 1 to compute counterfactual outcome of [Jacky, Mary], same for model 2

Student Gender Class Study Program Grade PSE

Terry male 1 1 82 0.7

Sarah female 2 1 83 0.6

Jacky male 1 0 78 0.7

Mary female 1 0 86 0.55

Page 36: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Other ML-based Methods

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❖ Representation learning

❖ Intuition: transform dataset into a space where treatment assignment is more evenly distributed

❖ For other more techniques, please check latest publications

Page 37: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Outline

37

Why Should Data Scientists Care?Basic ConceptsCausal Inference● Statistical Inference vs. Causal Inference● Average Treatment Effect (ATE) Estimation ● Causal Inference in Practice

Page 38: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Allocating Policy for Homelessness

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❖ Researchers allocate different interventions (like emergency shelter, rapid rehousing) for homelessness based on causal inference

❖ Report published in AAAI 2019

Amanda Kube, Sanmay Das, Patrick J. Fowler: Allocating Interventions Based on Predicted Outcomes: A Case Study on Homelessness Services. AAAI 2019: 622-629

Page 39: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Social Media

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❖ For Twitter, the impact of race, gender and closeness on persuasion is studied using causal inference

❖ Result published in NeurIPS 2019

https://cpb-us-w2.wpmucdn.com/sites.coecis.cornell.edu/dist/a/238/files/2019/12/Id_104_final.pdf

Page 40: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Natural Science

40

❖ Valuable conclusions about Archaeology are drawn from observational data using causal inference techniques

❖ Result published in KDD 2018

Biwei Huang, Kun Zhang, Yizhu Lin, Bernhard Schölkopf, Clark Glymour: Generalized Score Functions for Causal Discovery. KDD 2018: 1551-1560

Page 41: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

DOWhy Python Library

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❖ DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions, developed by Microsoft.

❖ DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

https://github.com/microsoft/dowhy

Page 42: Causal Inference · Natural Science 40 Valuable conclusions about Archaeology aredrawn from observational data using causal inference techniques Result published in KDD 2018 BiweiHuang,

Summary

42

Why Should Data Scientists Care?● “What if” Question ?● Why not A/B testing?● Causality ≠ Correlation

Basic Concepts● Outcome / Treatment Variables● Intervention and Do Operation● Counterfactual● Causal Graph

Causal Inference● Statistical Inference vs. Causal Inference● Average Treatment Effect (ATE) Estimation ● Causal Inference in Practice


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