Counterfactuals and Causal Inference Methods and Principles for Social Research

Name: Counterfactuals and Causal Inference Methods and Principles for Social Research
Price: 13.84 USD
Availability: InStock
ISBN: 9780521671934

ISBN-10: 0521671930

ISBN-13: 9780521671934

Edition: 2007

Authors: Stephen L. Morgan, Christopher Winship

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 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…

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
Language: English




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