Open Access
March 2018 Regularization and Confounding in Linear Regression for Treatment Effect Estimation
P. Richard Hahn, Carlos M. Carvalho, David Puelz, Jingyu He
Bayesian Anal. 13(1): 163-182 (March 2018). DOI: 10.1214/16-BA1044


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.


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P. Richard Hahn. Carlos M. Carvalho. David Puelz. Jingyu He. "Regularization and Confounding in Linear Regression for Treatment Effect Estimation." Bayesian Anal. 13 (1) 163 - 182, March 2018.


Published: March 2018
First available in Project Euclid: 11 January 2017

zbMATH: 06873722
MathSciNet: MR3737947
Digital Object Identifier: 10.1214/16-BA1044

Keywords: Causal inference , observational data , shrinkage estimation

Vol.13 • No. 1 • March 2018
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