Handbook of Causal Analysis for Social Research word 网盘 高速 下载地址大全 免费

Causal inference is a fundamental goal of social research, and it has been a topic of methodological research for decades. The evaluation of social science theory cannot proceed without assessing the sizes of entailed cause-effect relationships. Policy research cannot be conducted without estimating the impacts that follow from policy interventions. Unfortunately, for most social science research, controlled experimentation is not possible. And, when experimentation is feasible, it is often only possible in artificial contexts and for subjects who are not the representative of the target populations for inference. Tremendous progress has been made in the past 15 years in the causal analysis of non-experimental data, also known as observational data. The proposed handbook aims to explain this progress and then demonstrate how to use state-of-the-art methods for causal analysis in basic and applied empirical scholarship. The methods involve defining causal contrasts using counterfactual definitions and then estimating differences across individuals while maintaining clear assumptions about these contrasts. This approach allows for advanced forms of regression and multivariate case-matching, as well longitudinal differencing techniques, and instrumental variable estimation based on the occurrence of natural experiments. In the tradition that will be explicated in this handbook, substantial attention will also be devoted to representing underlying assumptions using causal graphs.

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.-

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

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