Is regression adjustment supported by the Neyman model for causal inference?

This paper examines both theoretically and empirically whether the common practice of using OLS multivariate regression models to estimate average treatment effects (ATEs) under experimental designs is justified by the Neyman model for causal inference. Using data from eight large U.S. social policy...

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Bibliographic Details
Published in:Journal of statistical planning and inference 2010, Vol.140 (1), p.246-259
Main Author: Schochet, Peter Z.
Format: Article
Language:English
Subjects:
Average treatment effects
Exact sciences and technology
Experimental design
Experimental designs
General topics
Linear inference, regression
Logic and foundations
Mathematical logic, foundations, set theory
Mathematics
Model theory
Neyman causal model
Probability and statistics
Regression adjustment
Sciences and techniques of general use
Social policy interventions
Statistics
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Summary:This paper examines both theoretically and empirically whether the common practice of using OLS multivariate regression models to estimate average treatment effects (ATEs) under experimental designs is justified by the Neyman model for causal inference. Using data from eight large U.S. social policy experiments, the paper finds that estimated standard errors and significance levels for ATE estimators are similar under the OLS and Neyman models when baseline covariates are included in the models, even though theory suggests that this may not have been the case. This occurs primarily because treatment effects do not appear to vary substantially across study subjects.
ISSN:0378-3758
1873-1171
DOI:10.1016/j.jspi.2009.07.008