This book reviews the tremendous progress of the past 15 years in the causal analysis of non-experimental data, also known as observational data, and shows how to use state-of-the-art methods for causal analysis in basic and applied empirical scholarship.

What constitutes a causal explanation, and must an explanation be causal? What warrants a causal inference, as opposed to a descriptive regularity? What techniques are available to detect when causal effects are present, and when can these techniques be used to identify the relative importance of these effects? What complications do the interactions of individuals create for these techniques? When can mixed methods of analysis be used to deepen causal accounts? Must causal claims include generative mechanisms, and how effective are empirical methods designed to discover them? The Handbook of Causal Analysis for Social Research tackles these questions with nineteen chapters from leading scholars in sociology, statistics, public health, computer science, and human development.

Addresses the main debates in the methodology of social inquiry Offers overview material on causal inference and covers many central issues in the identification of systems of causal relationships Brings together leading scholars in Sociology, Statistics, Public Health, Computer Science and Human Development

Autorentext
Stephen L. Morgan is the Jan Rock Zubrow '77 Professor in the Social Sciences at Cornell University, Ithaca, NY, USA.

Inhalt

Preface.- Chapter 1. Introduction; Stephen L. Morgan.- Part I. Background and Approaches to Analysis.- Chapter 2. A History of Causal Analysis in the Social Sciences; Sondra N. Barringer, Erin Leahey and Scott R. Eliason.- Chapter 3. Types of Causes; Jeremy Freese and J. Alex Kevern.- Part II. Design and Modeling Choices.- Chapter 4. Research Design: Toward a Realistic Role for Causal Analysis; Herbert L. Smith.- Chapter 5. Causal Models and Counterfactuals; James Mahoney, Gary Goertz and Charles C. Ragin.- Chapter 6. Mixed Models and Counterfactuals; David J. Harding and Kristin S. Seefeldt.- Part III. Beyond Conventional Regression Models.- Chapter 7. Fixed Effects, Random Effects, and Hybrid Models for Causal Analysis; Glenn Firebaugh, Cody Warner, and Michael Massoglia.- Chapter 8. Heteroscedastic Regression Models for the Systematic Analysis of Residual Variance; Hui Zheng, Yang Yang and Kenneth C. Land.- Chapter 9. Group Differences in Generalized Linear Models; Tim F. Liao.-Chapter 10. Counterfactual Causal Analysis and Non-Linear Probability Models; Richard Breen and Kristian Bernt Karlson.- Chapter 11. Causal Effect Heterogeneity; Jennie E. Brand and Juli Simon Thomas.- Chapter12. New Perspectives on Causal Mediation Analysis; Xiaolu Wang and Michael E. Sobel.- Part IV. Systems and Causal Relationships.- Chapter 13. Graphical Causal Models; Felix Elwert.- Chapter 14. The Causal Implications of Mechanistic Thinking: Identification Using Directed Acyclic Graphs (DAGs); Carly R. Knight and Christopher Winship.- Chapter 15. Eight Myths about Causality and Structural Equation Models; Kenneth A. Bollen and Judea Pearl.- Part V. Influence and Interference.- Chapter 16. Heterogeneous Agents, Social Interactions, and Causal Inference; Guanglei Hong and Stephen W. Raudenbush.- Chapter 17. Social Networks and Causal Inference; Tyler J. VanderWeele and Weihua An.- Part VI. Retreat From Effect Identification.- Chapter 18. Partial Identification and Sensitivity Analysis; Markus Gangl.- Chapter 19. What You can Learn from Wrong Causal Models; Richard Berk, Lawrence Brown, Edward George, Emil Pitkin, Mikhail Traskin, Kai Zhang and Linda Zhao.