Covariate selection with group lasso and doubly robust estimation of causal effects.

ABSTRACT: The efficiency of doubly robust estimators of the average causal effect (ACE) of a treatment can be improved by including in the treatment and outcome models only those covariates which are related to both treatment and outcome (i.e., confounders) or related only to the outcome. However, it is often challenging to identify such covariates among the large number that may be measured in a given study. In this article, we propose GLiDeR (Group Lasso and Doubly Robust Estimation), a novel variable selection technique for identifying confounders and predictors of outcome using an adaptive group lasso approach that simultaneously performs coefficient selection, regularization, and estimation across the treatment and outcome models. The selected variables and corresponding coefficient estimates a

SUBMITTER: Koch B 

PROVIDER: S-EPMC5740021 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature