Morgan Stephen L. Handbook of Causal Analysis for Social Research

Springer, 2013. — 423 p. — ISBN: 9400760930.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.Background and Approaches to Analysis
A History of Causal Analysis in the Social Sciences
Types of Causes
Design and Modeling Choices
Research Design:Toward a Realistic Role for Causal Analysis
Causal Models and Counterfactuals
Mixed Models and Counterfactuals
Beyond Conventional Regression Models
Fixed Effects, Random Effects, and Hybrid Models for Causal Analysis
Heteroscedastic Regression Models for the Systematic Analysis of Residual Variance
Group Differences in Generalized Linear Models
Counterfactual Causal Analysis and Non-Linear Probability Models
Causal Effect Heterogeneity
New Perspectives on Causal Mediation Analysis
Systems and Causal Relationships
Graphical Causal Models
The Causal Implications of Mechanistic Thinking: Identification Using Directed Acyclic Graphs (DAGs
Eight Myths about Causality and Structural Equation Models
Influence and Interference
Heterogeneous Agents, Social Interactions, and Causal Inference
Social Networks and Causal Inference
Retreat from Effect Identification
Partial Identification and Sensitivity Analysis
What You can Learn from Wrong Causal Models