Causal Inference in Python: Applying Causal Inference in the Tech Industry (Fourth Early Release)

Preface
        Prerequisites
        Data and Code
        Outline
        Conventions Used in This Book
        Using Code Examples
        O’Reilly Online Learning
        How to Contact Us
        Acknowledgments
    I. Fundamentals
    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 Stable Unit Treatment Values
            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
        Key Ideas
    2. Randomized Experiments and Stats Review
        Brute Force Independence with Randomization
        An A/B Testing Example
        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
    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
            Surrogate Confounding
            Randomization Revisited
        Selection Bias
            Conditioning on a Collider
            Adjusting for Selection Bias
            Conditioning on a Mediator
        Key Ideas
    II. Adjusting for Bias
    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
    5. Propensity Score
        The Impact of Management Training
        Adjusting with Regression
        Propensity Score
            Propensity Score Estimation
            Propensity Score and Orthogonalization
            Propensity Score Matching
            Inverse Propensity Weighting
            Variance of IPW
            Stabilized Propensity Weights
            Pseudo-Populations
            Selection Bias
            Bias-Variance Trade-Off
            Positivity
        Design vs Model-Based Identification
        Doubly Robust Estimation
            Treatment is Easy to Model
            Outcome is Easy to Model
        Generalized Propensity Score for Continuous Treatment
        Keys Ideas
    III. Effect Heterogeneity and Personalization
    6. Effect Heterogeneity
        From ATE to CATE
        Why Prediction is not the Answer
        CATE with Regression
        Evaluating CATE Predictions
        Effect by Model Quantile
        Cumulative Effect
        Cumulative Gain
        Target Transformation
        When Prediction Models are Good for Effect Ordering
            Marginal Decreasing Returns
            Binary Outcomes
        CATE for Decision Making
        Key Ideas
    7. Meta-Learners
        Meta-Learners for Discrete Treatments
            T-Learner
            X-Learner
        Meta-Learners for Continuous Treatments
            S-Learner
            Double/Debiased Machine Learning
        Key Ideas
    IV. Panel Data
    8. Difference-in-Differences
        Panel Data
        Canonical Difference-in-Differences
            Diff-in-Diff with Outcome Growth
            Diff-in-Diff with OLS
            Diff-in-Diff with Fixed Effects
            Multiple Time Periods
            Inference
        Identification Assumptions
            Parallel Trends
            No Anticipation Assumption and SUTVA
            Strict Exogeneity
            No Time Varying Confounders
            No Feedback
            No Carryover and no Lagged Dependent Variable
        Effect Dynamics Over Time
        Diff-in-Diff with Covariates
        Doubly Robust Diff-in-Diff
            Propensity Score Model
            Delta Outcome Model
            All Together Now
        Staggered Adoption
            Heterogeneous Effect over Time
            Covariates
        Key Ideas
    9. Synthetic-Control
        Online Marketing Dataset
        Matrix Representation
        Synthetic Control as Horizontal Regression
        Canonical Synthetic Control
        Synthetic Control with Covariants
        Debiasing Synthetic Control
        Inference
        Synthetic Difference-in-Differences
            DID Refresher
            Synthetic Controls Revisited
            Estimating Time Weights
            Synthetic Control and DID
        Key Ideas
    V. Alternative Experimental Designs
    10. Geo and Switchback Experiments
        Geo-Experiments
        Synthetic Control Design
            Trying a Random Set of Treated Units
            Random Search
        Switchback Experiment
            Potential Outcomes of Sequences
            Estimating the Order of Carryover Effect
            Design Based Estimation
            Optimal Switchback Design
            Robust Variance
        Key Ideas
    11. Non-Compliance and Instruments
        Non-Compliance
        Extending Potential Outcomes
        Instrument Identification Assumptions
        First Stage
        Reduced Form
        Two Stage Least Squares
        Standard Error
        Additional Controls and Instruments
            2SLS by Hand
            Matrix Implementation
        Discontinuity Design
            Discontinuity Design Assumptions
            Intention to Treat Effect
            The IV Estimate
            Bunching
        Key Ideas
    12. Next Steps
        Causal Discovery
        Sequential Decision Making
        Causal Reinforcement Learning
        Causal Forecasting
        Domain Adaptation
        Closing Thoughts
    Index
    About the Author