Bayesian Analysis

Regularization and Confounding in Linear Regression for Treatment Effect Estimation

P. Richard Hahn, Carlos M. Carvalho, David Puelz, and Jingyu He

Full-text: Open access

Abstract

This paper investigates the use of regularization priors in the context of treatment effect estimation using observational data where the number of control variables is large relative to the number of observations. First, the phenomenon of “regularization-induced confounding” is introduced, which refers to the tendency of regularization priors to adversely bias treatment effect estimates by over-shrinking control variable regression coefficients. Then, a simultaneous regression model is presented which permits regularization priors to be specified in a way that avoids this unintentional “re-confounding”. The new model is illustrated on synthetic and empirical data.

Article information

Source
Bayesian Anal. Volume 13, Number 1 (2018), 163-182.

Dates
First available in Project Euclid: 11 January 2017

Permanent link to this document
https://projecteuclid.org/euclid.ba/1484103680

Digital Object Identifier
doi:10.1214/16-BA1044

Keywords
causal inference observational data shrinkage estimation

Rights
Creative Commons Attribution 4.0 International License.

Citation

Hahn, P. Richard; Carvalho, Carlos M.; Puelz, David; He, Jingyu. Regularization and Confounding in Linear Regression for Treatment Effect Estimation. Bayesian Anal. 13 (2018), no. 1, 163--182. doi:10.1214/16-BA1044. https://projecteuclid.org/euclid.ba/1484103680


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