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Counterfactuals and Causal Inference Methods and Principles for Social Research

ISBN-10: 0521671930
ISBN-13: 9780521671934
Edition: 2007
List price: $34.99
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Description: In this book, the essential features of the counterfactual model of causality for observational data analysis are presented with examples from sociology, political science, and economics. Alternative estimation techniques are first introduced using  More...

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Book details

List price: $34.99
Copyright year: 2007
Publisher: Cambridge University Press
Publication date: 7/30/2007
Binding: Paperback
Pages: 328
Size: 6.00" wide x 8.75" long x 0.75" tall
Weight: 0.990

In this book, the essential features of the counterfactual model of causality for observational data analysis are presented with examples from sociology, political science, and economics. Alternative estimation techniques are first introduced using causal graphs, and conditioning techniques such as matching and regression are presented from a potential outcomes perspective. For research scenarios in which important determinants of causal exposure are unobserved, alternative techniques such as instrumental variable estimators, longitudinal methods, and estimation via causal mechanisms are then presented. The importance of causal effect heterogeneity is stressed throughout the book and the need for deep causal explanation via mechanisms is discussed.

Counterfactual Causality and Empirical Research in the Social Sciences
Introduction
The Counterfactual Model for Observational Data Analysis
Causal Analysis and Observational Social Science
Types of Examples Used Throughout the Book
Observational Data and Random-Sample Surveys
Identification and Statistical Inference
Causal Graphs as an Introduction to the Remainder of the Book
The Counterfactual Model
Causal States and Potential Outcomes
Treatment Groups and Observed Outcomes
The Average Treatment Effect
The Stable Unit Treatment Value Assumption
Treatment Assignment and Observational Studies
Average Causal Effects and Naive Estimation
Conclusions
Population and Data Generation Models
Extension of the Framework to Many-Valued Treatments
Estimating Causal Effects by Conditioning
Causal Graphs, Identification, and Models of Causal Exposure
Causal Graphs and Conditioning as Back-Door Identification
Models of Causal Exposure in the Counterfactual Tradition
Conditioning to Balance versus Conditioning to Adjust
Point Identification of Conditional Average Treatment Effects by Conditioning
Conclusions
Matching Estimators of Causal Effects
Origins of and Motivations for Matching
Matching as Conditioning via Stratification
Matching as Weighting
Matching as a Data Analysis Algorithm
Matching When Treatment Assignment is Nonignorable
Remaining Practical Issues in Matching Analysis
Conclusions
Regression Estimators of Causal Effects
Regression as a Descriptive Tool
Regression Adjustment as a Strategy to Estimate Causal Effects
The Connections Between Regression and Matching
Extensions and Other Perspectives
Conclusions
Estimating Causal Effects When Simple Conditioning is Ineffective
Identification in the Absence of a Complete Model of Causal Exposure
Nonignorability and Selection on the Unobservables Revisited
Sensitivity Analysis for Provisional Causal Effect Estimates
Partial Identification with Minimal Assumptions
Additional Strategies for the Point Identification of Causal Effects
Conclusions
Appendix: Latent Variable Selection-Bias Models
Instrumental Variable Estimators of Causal Effects
Causal Effect Estimation with a Binary IV
Traditional IV Estimators
Recognized Pitfalls of Traditional IV Estimation
Instrumental Variable Estimators of Average Causal Effects
Two Additional Perspectives on the Identification of Causal Effects with IVs
Conclusions
Mechanisms and Causal Explanation
The Dangers of Insufficiently Deep Explanations
Explanation and Identification of Causal Effects by Mechanisms
The Appeal for Generative Mechanisms
The Pursuit of Explanation by Mechanisms that Bottom Out
Conclusion
Repeated Observations and the Estimation of Causal Effects
Interrupted Time Series Models
Regression Discontinuity Designs
Panel Data
Conclusions
Conclusions
Counterfactual Causality and Future Empirical Research in the Social Sciences
Objections to Features of the Counterfactual Model
Modes of Causal Inquiry in the Social Sciences
References
Index

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