Causal Inference: for Statistics,Social, and Biomedical Science/ Guido W. Imbens and Donald B Rubin

Include Index

Causality : The Basic Framework -- A Brief History of the Potential Outcomes Approach to Casual Inference -- Classification of Assignment Mechanisms -- 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 -- 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 Distributions -- Subclassification on the Propensity Score -- Matching Estimators -- A General Method for Estimating Sampling Variances for Standard Estimators for Average Casual Effects -- Inference for General Casual Estimands -- Assessing Unconfoundedness -- Sensitivity Analysis and Bounds -- Instrumental Variables Analysis of Randomized Experiments with One-Side Noncompliance -- Instrumental Variables Analysis of Randomized Experiments with Two-Side Noncompliance -- Mood Based Analysis in Instrumental Variable Settings : Randomized Experiments with Two-Side Noncompliance -- Conclusions and Extentions

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