Robust and efficient semi-supervised estimation of average treatment effects with application to electronic health records data.

ABSTRACT: We consider the problem of estimating the average treatment effect (ATE) in a semi-supervised learning setting, where a very small proportion of the entire set of observations are labeled with the true outcome but features predictive of the outcome are available among all observations. This problem arises, for example, when estimating treatment effects in electronic health records (EHR) data because gold-standard outcomes are often not directly observable from the records but are observed for a limited number of patients through small-scale manual chart review. We develop an imputation-based approach for estimating the ATE that is robust to misspecification of the imputation model. This effectively allows information from the predictive features to be safely leveraged to improve efficiency

SUBMITTER: Cheng D 

PROVIDER: S-EPMC7758040 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature