Table of Contents

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  • Preface  
  • Acknowledgements
  • Organization of this Book
  • I. Preliminaries
    • 1. Questions about Questions    
    • 2. The Experimental Ideal
      • 2.1 The Selection Problem
      • 2.2 Random Assignment Solves the Selection Problem
      • 2.3 Regression Analysis of Experiments
  • II. The Core
    • 3. Making Regression Make Sense
      • 3.1 Regression Fundamentals
        • 3.1.1 Economic Relationships and the Conditional Expectation Function
        • 3.1.2 Linear Regression and the CEF
        • 3.1.3 Asymptotic OLS Inference
        • 3.1.4 Saturated Models, Main Effects, and Other Regression Talk
      • 3.2 Regression and Causality
        • 3.2.1 The Conditional Independence Assumption
        • 3.2.2 The Omitted Variables Bias Forumla
        • 3.2.3 Bad Control
      • 3.3 Heterogeneity and Nonlinearity
        • 3.3.1 Regression Meets Matching
        • 3.3.2 Control for Covariates Using Propensity Score
        • 3.3.3 Propensity-Score Methods vs. Regression
      • 3.4 Regression Details
        • 3.4.1 Weighting Regression
        • 3.4.2 Limited Dependent Variables and Marginal Effects
        • 3.4.3 Why is Regression Called Regression and What Does Regression-to-the-mean Mean?
      • 3.5 Appendix: Derivation of the Average Derivative Weighting Function
    • 4. Instrumental Variables in Action: Sometimes You Get What You Need
      • 4.1 IV and Causality
        • 4.1.1 Two-Stage Least Squares
        • 4.1.2 The Wald Estimator
        • 4.1.3 Grouped Data and 2SLS
      • 4.2 Asymptotic 2SLS Inference
        • 4.2.1 The Limiting Distribution of the 2SLS Coefficient Vector
        • 4.2.2 Over-identification and the 2SLS Minimand
      • 4.3 Two-Sample IV and Split-Sample IV
      • 4.4 IV with Heterogeneous Potential Outcomes
        • 4.4.1 Local Average Treatment Effects
        • 4.4.2 The Complaint Subpopulation
        • 4.4.3 IV in Randomized Trials
        • 4.4.4 Counting and Characterizing Compliers
      • 4.5 Generalizing LATE
        • 4.5.1 LATE with Multiple Instruments
        • 4.5.2 Covariates in the Heterogeneous-effects Model
        • 4.5.3 Average Casual Response with Variable Treatment Intensity
      • 4.6 IV Details
        • 4.6.1 2SLS Mistakes
        • 4.6.2 Peer Effects
        • 4.6.3 Limited Dependent Variables Reprise
        • 4.6.4 The Bias of 2SLS
      • 4.7 Appendix
    • 5. Parallel Worlds: Fixed Effects, Differences-in-Differences, and Panel Data
      • 5.1 Individual Fixed Effects
      • 5.2 Differences-in-Differences
        • 5.2.1 Regression DD
      • 5.3 Fixed Effects versus Lagged Dependent Variables
      • 5.4 Appendix: More on Fixed Effects and Lagged Dependent Variables
  • III. Extensions
    • 6. Getting a Little Jumpy: Regression Discontinuity Designs
      • 6.1 Sharp RD
      • 6.2 Fuzzy RD is IV
    • 7. Quantile Regression
      • 7.1 The Quantile Regression Model
        • 7.1.1 Censored Quantile Regression
        • 7.1.2 The Quantile Regression Approximation Property
        • 7.1.3 Tricky Points
      • 7.2 Quantile Treatment Effects
        • 7.2.1 The QTE Estimator
    • 8. Nonstandard Standard Error Issues
      • 8.1 The Bias of Robust Standard Errors
      • 8.2 Clustering and Serial Correlation in Panels
        • 8.2.1 Clustering and the Moulton Factor
        • 8.2.2 Serial Correlation in Panels and Difference-in-Difference Models
        • 8.2.3 Fewer than 42 Clusters
      • 8.3 Appendix: Derivation of the Simple Moulton Factor
  • Last Words
  • Acronyms and Abbreviations
  • Empirical Studies Index
  • References
  • Index