Causal Inference in Python

✍ Scribed by Matheus Facure

• Publisher: O'Reilly Media, Inc.
• Year: 2022
• Tongue: English
• Leaves: 176
• Category: Library

✦ Synopsis

How many buyers will an additional dollar of online marketing bring in? Which customers will only buy when given a discount coupon? How do you establish an optimal pricing strategy? The best way to determine how the levers at our disposal affect the business metrics we want to drive is through causal inference.

In this book, author Matheus Facure, senior data scientist at Nubank, explains the largely untapped potential of causal inference for estimating impacts and effects. Managers, data scientists, and business analysts will learn classical causal inference methods like randomized control trials (A/B tests), linear regression, propensity score, synthetic controls, and difference-in-differences. Each method is accompanied by an application in the industry to serve as a grounding example.

✦ Table of Contents

Preface
Prerequisites
Outline
1. Introduction To Causal Inference
What is Causal Inference
Why we Do Causal Inference
Machine Learning and Causal Inference
Association and Causation
The Treatment and the Outcome
The Fundamental Problem of Causal Inference
Causal Models
Interventions
Individual Treatment Effect
Potential Outcomes
Consistency and No Interference Assumptions
Causal Quantities of Interest
Causal Quantities: An Example
Bias
The Bias Equation
A Visual Guide to Bias
Identifying the Treatment Effect
The Independence Assumption
Identification with Randomization
Chapter Key Ideas
Other Examples
A Glass of Wine a Day Keeps the Doctor Away
An Incredible Membership Program
2. Randomized Experiments and Stats Review
Brute Force Independence with Randomization
An A/B Testing Example
Checking for Balance
The Ideal Experiment
The Most Dangerous Equation
The Standard Error of Our Estimates
Confidence Intervals
Hypothesis Testing
Null Hypothesis
Test Statistic
P-values
Power
Sample Size Calculation
Key Ideas
Other Examples
The Effectiveness of COVID19 Vaccines
Face-to-Face vs Online Learning.
3. Graphical Causal Models
Thinking About Causality
Visualizing Causal Relationships
Are Consultants Worth it?
Crash Course in Graphical Models
Chains
Forks
Immorality or Collider
The Flow of Association Cheat Sheet
Querying a Graph in Python
Identification Revisited
CIA and The Adjustment Formula
Positivity Assumption
An Identification Example with Data
Confounding Bias
Randomization Revisited
Selection Bias
Conditioning on a Collider
Adjusting for Selection Bias
Conditioning on a Mediator
Key Ideas
Other Examples
Conditioning on the Positives
The Hidden Bias in Survival Analysis
4. The Unreasonable Effectiveness of Linear Regression
All You Need is Linear Regression
Why We Need Models
Regression in A/B Tests
Adjusting with Regression
Regression Theory
Single Variable Linear Regression
Multivariate Linear Regression
Frisch-Waugh-Lovell Theorem and Orthogonalization
Debiasing Step
Denoising Step
Standard Error of the Regression Estimator
Final Outcome Model
FWL Summary
Regression as an Outcome Model
Positivity and Extrapolation
Non-Linearities in Linear Regression
Linearizing the Treatment
Non-Linear FWL and Debiasing
Regression for Dummies
Conditionally Random Experiments
Dummy Variables
Saturated Regression Model
Regression as Variance Weighted Average
De-Meaning and Fixed Effects
Omitted Variable Bias: Confounding Through the Lens of Regression
Neutral Controls
Noise Inducing Control
Feature Selection: A Bias-Variance Trade-Off
Key Ideas
Other Examples
Public or Private Schools?
Marketing Mix Modeling
5. Propensity Score
The Impact of Management Training
Adjusting with Regression
Propensity Score
Propensity Score Estimation
Propensity Score and Orthogonalization
Inverse Propensity Weighting
Variance of IPW
Stabilized Propensity Weights
Pseudo-Populations
Selection Bias
Bias-Variance Trade-Off
Positivity
Doubly Robust Estimation
Treatment is Easy to Model
Outcome is Easy to Model
Generalized Propensity Score for Continuous Treatment
Keys Ideas
Other Examples
Causal Contextual Bandits