Includes bibliographical references and index

Part 1: Introduction -- Causality: The Basic Framework -- A Brief History of the Potential Outcomes Approach to Causal Inference -- A Classification of Assignment Mechanisms -- Part 2: Classical Randomized Experiments -- A Taxonomy of Classical Randomized Experiments -- Fisher's Exact P-Values for Completely Randomized Experiments -- Neyman's Repeated Sampling Approach to Completely Randomized Experiments -- Regression Methods for Completely Randomized Experiments -- Model-Based Inference for Completely Randomized Experiments -- Stratified Randomized Experiments -- Pairwise Randomized Experiments -- Case Study: An Experimental Evaluation of a Labor Market Program -- Part 3: Regular Assignment Mechanisms: Design -- Unconfounded Treatment Assignment -- Estimating the Propensity Score -- Assessing Overlap in Covariate Distributions -- Matching to Improve Balance in Covariate Distributions -- Trimming to Improve Balance in Covariate Distribution -- Part 4: Regular Assignment Mechanisms: Analysis -- Subclassification on the Propensity Score -- Matching Estimators -- A General Method for Estimating Sampling Variances for Standard Estimators for Average Causal Effects -- Inference for General Causal Estimands -- Part 5: Regular Assignment Mechanisms: Supplementary Analyses -- Assessing Unconfoundedness -- Sensitivity Analysis and Bounds -- Part 6: Regular Assignment Mechanisms with Noncompliance: Analysis -- Instrumental Variables Analysis of Randomized Experiments with One-Sided Noncompliance -- Instrumental Variables Analysis of Randomized Experiments with Two-Sided Noncompliance -- Model-Based Analysis in Instrumental Variable Settings: Randomized Experiments with Two-Sided Noncompliance -- Part 7: Conclusion -- Conclusions and Extensions