Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher. This book is divided into seven parts. The table of contents presents them as follows: Part I, Introduction, presents the following chapters: (1) Causality, The Basic Framework; (2) A Brief History of the Potential Outcomes Approach to Causal Inference;and (3) A Classification of Assignment Mechanisms. Part II, Classical Randomized Experiments, contains: (4) A Taxonomy of Classical Randomized Experiments; (5) Fisher's Exact P-Values for Completely Randomized Experiments; (6) Neyman's Repeated Sampling Approach to Completely Randomized Experiments; (7) Regression Methods for Completely Randomized Experiments; (8) Model- Based Inference for Completely Randomized Experiments; (9) Stratified Randomized Experiments; (10) Pairwise Randomized Experiments; and (11) Case Study: An Experimental Evaluation of a Labor Market Program. Part III, Regular Assignment Mechanisms: Design, contains: (12) Unconfounded Treatment Assignment; (13) Estimating the Propensity Score; (14) Assessing Overlap in Covariate Distributions; (15) Matching to Improve Balance in Covariate Distributions; and (16) Trimming to Improve Balance in Covariate Distributions. Part IV, Regular Assignment Mechanisms: Analysis, contains: (17) Subclassification on the Propensity Score; (18) Matching Estimators; (19) A General Method for Estimating Sampling Variances for Standard Estimators for Average Causal Effects; and (20) Inference for General Causal Estimands. Part V, Regular Assignment Mechanisms: Supplementary Analysis, contains: (21) Assessing Unconfoundness; and (22) Sensitivity Analysis and Bounds. Part VI, Regular Assignment with NonCompliance: Analysis, contains (23) Instrumental Variables Analysis of Randomized Experiments with One-Sided Noncompliance; (24) Instrumental Variables Analysis of Randomized Experiments with Two-Sided Noncompliance; and (25) Model-Based Analysis in Instrumental Variable Settings: Randomized Experiments with Two-Sided Noncompliance. Part VII, Conclusion, contains: (26) Conclusions and Extensions. References and an Index are also provided.