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.-

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.-