Synopses & Reviews
In this book, the essential features of the counterfactual model of causality for observational data analysis are presented with examples from sociology, political science, and economics. Alternative estimation techniques are first introduced using causal graphs, and conditioning techniques such as matching and regression are presented from a potential outcomes perspective. For research scenarios in which important determinants of causal exposure are unobserved, alternative techniques such as instrumental variable estimators, longitudinal methods, and estimation via causal mechanisms are then presented. The importance of causal effect heterogeneity is stressed throughout the book and the need for deep causal explanation via mechanisms is discussed.
'The essential features of the counterfactual model of causality for observational data analysis are presented with examples.'
Did mandatory busing programs in the 1970s increase the school achievement of disadvantaged minority youth? Does obtaining a college degree increase an individual s labor market earnings? Did the use of a butterfly ballot in some Florida counties in the 2000 presidential election cost Al Gore votes? Simple cause-and-effect questions such as these are the motivation for much empirical work in the social sciences. In this book, the counterfactual model of causality for observational data analysis is presented, and methods for causal effect estimation are demonstrated using examples from sociology, political science, and economics.
Table of Contents
Part I. Counterfactual Causality and Empirical Research in the Social Sciences: 1. Introduction; 2. The counterfactual model; Part II. Estimating Causal Effects by Conditioning: 3. Causal graphs, identification, and models of causal exposure; 4. Matching estimators of causal effects; 5. Regression estimators of causal effects; Part III. Estimating Causal Effects When Simple Conditioning is Ineffective: 6. Identification in the absence of a complete model of causal exposure; 7. Natural experiments and instrumental variables; 8. Mechanisms and causal explanation; 9. Repeated observations and the estimation of causal effects; Part IV. Conclusions: 10. Counterfactual causality and future empirical research in the social sciences.