PREFACE CHAPTER 1 - Introduction 1.1 Drawing Inferences from Intuitions, Anecdotes, and Correlations 1.2 Experimentes as a Solution to the Problem of Unobserved Confounders 1.3 Experiments as Fair Tests 1.4 Field Experiments 1.5 Advantages and Disadvantages of Experiementing in Real-World Settings 1.6 Naturally Occurring Expertiments and Quasi-Experiments 1.7 Plan of the Book CHAPTER 2 - Causal Inference and Experimentation 2.1 Potential Outcomes 2.2 Average Treatment Effects 2.3 Random Sampling and Expectations 2.4 Random Assignment and Unbiased Inference 2.5 The Mechanics of Random Assignment 2.6 The Threat of Selection Bias When Random Assignment Is Not Used 2.7 Two Core Assumptions about Potential Outcomes CHAPTER 3 - Sampling Distributions, Statistical Inference, and Hypothesis Testing 3.1 Sampling Distributions 3.2 The Standard Error as a Measure of Uncertainty 3.3 Estimating Sampling Variability 3.4 Hypothesis Testing 3.5 Confidence Intervals 3.6 Sampling Distributions for Experiments That Use Block or Cluster Random Assignment CHAPTER 4 - Using Covariates in Experimental Design and Analysis 4.1 Using Covariates to Rescale Outcomes 4.2 Adjusting for Covariates Using Regression 4.3 Covariate Imbalance and the Detection of Administrative Errors 4.4 Blocked Randomization and Covariate Adjustment 4.5 Analysis of Block Randomized Experiments with Treatment Probabilities That Vary by Block CHAPTER 5 - One-Sided Noncompliance 5.1 New Definitions and Assumptions 5.2 Defining Causal Effects for the Case of One-Sided Noncompliance 5.3 Average Treatment Effects, Intent-To-Treat Effects, and Complier Average Causal Effects 5.4 Indentification of the CACE 5.5 Estimation 5.6 Avoiding Common Mistakes 5.7 Evaluating the Assumptions Required to Identify the CACE 5.8 Statistical Inference 5.9 Designing Experiments in Anticipation of Noncompliance 5.10 Estimating Treatment Effects When Some Subjects Receive "Partial Treatment" CHAPTER 6 - Two-Sided Noncompliance 6.1 Two-Sided Noncompliance: New Definitions and Assumptions 6.2 ITT, ITT, and CACE under Two-Sided Noncompliance 6.3 A Numerical Illustration of the Role of Monotonicity 6.4 Estimation of the CACE: An Example 6.5 Discussion of Assumptions 6.6 Downstream Experimentation CHAPTER 7 - Attrition 7.1 Conditions Under Which Atrition Leads to Bias 7.2 Special Forms of Attrition 7.3 Redefining the Estimand When Attrition Is Not a Function of Treatment Assignment 7.4 Placing Bounds on the Average Treatment Effect 7.5 Addressing Attrition: An Empirical Example 7.6 Addressing Attrition with Additional Data Collection 7.7 Two Frequently Asked Questions CHAPTER 8 - Interference between Experimental Units 8.1 Indentifying Causal Effects in the Presence of Localized Spillover 8.2 Spatil Spillover 8.3 An Example of Spatial Spillovers in Two Dimensions 8.4 Within-Subjects Design and Time-Series Experiments 8.5 Waitlist Designs (Also Known as Stepped-Wedge Designs) CHAPTER 9 - Heterogeneous Treatment Effects 9.1 Limits to What Experimental Data Tell Us about Treatment Effect Heterogeneity 9.2 Bounding Var (t) and Testing for Heterogeneity 9.3 Two Approaches to the Exploration of Heterogeneity: Covariates and Design 9.4 Using Regression to Model Treatment Effect Heterogeneity 9.5 Automating the Search for Interactions CHAPTER 10 - Mediation 10.1 Regression-Based Approaches to Mediation 10.2 Mediation Analysis from a Potential Outcomes Perspective 10.3 Why Experimental Analysis of Mediators Is Challenging 10.4 Ruling Out Mediators? 10.5 What about Experiments That Manipulate the Mediator? 10.6 Implicit Mediation Analysis CHAPTER 11 - Integration of Research Findings 11.1 Estimation of Population Average Treatment Effects 11.2 A Bayesian Framework for Interpreting Research Findings 11.3 Replication and Integration of Experimental Findings: An Example 11.4 Treatment That Vary in Intensity: Extrapolation and Statistical Modeling CHAPTER 12 - Instructive Examples of Experimental Design 12.1 Using Experimental Design to Distinguish between Competing Theories 12.2 Oversampling Subjects Based on Their Anticipated Response to Treatment 12.3 Comprehensive Measurement of Outcomes 12.4 Factorial Design and Special Cases of Non-Interface 12.5 Design and Analysis of Experiments In Which Treatments Vary with Subjects' Charecteristics 12.6 Design and Analysis of Experiments In Which Failure to Receive Treatment Has a Causal Effect 12.7 Addressing Complications Posed by Missing Data CHAPTER 13 - Writing a Proposal, Research Report, and Journal Article 13.1 Writing the Proposal 13.2 Writing the Research Report 13.3 Writing the Journal Article 13.4 Archiving Data APPENDIX A - Protection of Human Subjects A.1 Regulatory Guidelines A.2 Guidelines for Keeping Field Experiments wthin Regulatory Boundaries APPENDIX B - Suggested Field Experiments for Class Projects B.1 Crafting Your Own Experiment B.2 Suggested Experimental Topics for Practicum Exercises REFERENCES INDEX