European University Institute Library

Causal inference for statistics, social, and biomedical sciences, an introduction, Guido W. Imbens & Donald B. Rubin

eng

Includes bibliographical references ( pages 591-604) and indexes

no index present

non fiction

Causal inference for statistics, social, and biomedical sciences

933294021

Guido W. Imbens & Donald B. Rubin

an introduction

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.--, Provided by Publisher

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