Counterfactuals and Causal Inference: Methods and Principles for Social Research

by Stephen L. Morgan , Christopher Winship

• ISBN13: 9780521856157
• ISBN10: 0521856159
• Edition: 1st
• Format: Hardcover
• Copyright: 2007-07-30
• Publisher: Cambridge University Press

Summary

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 the butterfly ballot in some Florida counties in the 2000 presidential election cost Al Gore votes? If so, was the number of miscast votes sufficiently large to have altered the election outcome? At their core, these types of questions are simple cause-and-effect questions. Simple cause-and-effect questions are the motivation for much empirical work in the social sciences. This book presents a model and set of methods for causal effect estimation that social scientists can use to address causal questions such as these. The essential features of the counterfactual model of causality for observational data analysis are presented with examples from sociology, political science, and economics.

Author Biography

Stephen L. Morgan is Associate Professor of Sociology and the Director of the Center for the Study of Inequality at Cornell University. His previous publications include On the Edge of Commitment: Educational Attainment and Race in the United States (2005). Christopher Winship is Diker-Tishman Professor of Sociology at Harvard University. For the past twelve years he has served as editor of Sociological Methods and Research. He has published widely in a variety of journals and edited volumes.

Table of Contents

Counterfactual Causality and Empirical Research in the Social Sciences

Introduction

The counterfactual model

Estimating Causal Effects by Conditioning

Causal graphs, identification, and models of causal exposure

Matching estimators of causal effects

Regression estimators of causal effects

Estimating Causal Effects When Simple Conditioning is Ineffective

Identification in the absence of a complete model of causal exposure

Natural experiments and instrumental variables

Mechanisms and causal explanation

Repeated observations and the estimation of causal effects

Conclusions

Counterfactual causality and future empirical research in the social sciences

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