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Preface; Contents; About the Editor and Contributors; Editor; Contributors; Chapter 1: Introduction; Summary of Contents; Contribution; Part I: Background and Approaches to Analysis; Chapter 2: A History of Causal Analysis in the Social Sciences; Introduction; Regularity, Constant Conjunction, and the Birth of Configurational Causal Analysis; The Path to Structural Equation Modeling; Randomization, Experiments, and the Potential Outcomes Framework; Donald Rubin's Counterfactual Causal Model; James Heckman's Counterfactual Causal Model; Instrumental Variables and Related Methods
Max Weber and the Heart of Causal ComplexityThe Future of Causal Analysis in the Social Sciences; References; Chapter 3: Types of Causes; Counterfactuals, Predictability, and Manipulation; Causal Proximity; Causal Configurations; Causal Importance; Causes of Causal Relationships; Conclusion; References; Part II: Design and Modeling Choices; Chapter 4: Research Design: Toward a Realistic Role for Causal Analysis; Introduction; Three Perspectives from Statisticians; Leslie Kish on Randomization, Realism, and Representation; David Freedman on Research Designs for the Social Sciences
Paul Rosenbaum on Elaborating Causal TheoriesSummary; Effects of Causes and Causes of Effects; Why It Is Hard to Distinguish the Two: Causation of Cholera and of Autism; Delimiting Causes of Effects; Summary; The Experiment as the Model for Research Design; Heterogeneity of Treatment Effects; Causal Interpretations of Intervening Variables Consequent to Randomization; The Manipulation Criterion; Research Designs with Causes Operating Above the Individual Level; Interference Between Observations: SUTVA; Conclusion; References; Chapter 5: Causal Models and Counterfactuals; Causal Models
Additive-Linear Versus Set-Theoretic ModelsCausal Complexity; Equifinality; Asymmetry; Counterfactuals; Constructing and Using Counterfactuals in Statistics; Counterfactuals in the Set-Theoretic Approach; Conclusion; References; Chapter 6: Mixed Methods and Causal Analysis; Introduction; Causal Analysis in Qualitative Research; Multiple Roles for Qualitative Methods in Mixed Methods Causal Analysis; Elucidating Selection Processes; Mechanisms: The Why and How of Treatment Effects; Sources of Effect Heterogeneity; Understanding Variable Measurement from Survey and Administrative Data
Treatment Definition and Program FidelityIntegrating Qualitative and Quantitative Methods; Subject or Case Selection: Random Samples vs. Purposive Samples; Subject or Case Selection: Nested vs. Non-nested Samples; Sequencing of Qualitative and Quantitative Data Collection; Subject- vs. Researcher-Driven Approaches; Conclusion; References; Part III: Beyond Conventional Regression Models; Chapter 7: Fixed Effects, Random Effects, and Hybrid Models for Causal Analysis; Introduction; The Fixed Effects Model; What Is Sacrificed in Fixed Effects Models?; The Random Effects Model
Hybrid Model: The Centered Random Effects Model
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.
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 Anlaysis for Social Research tackles these questions with nineteen chapters from leading scholars in sociology, statistics, public health, computer science, and human development.
