Robust and Efficient Semi-Supervised Estimation of Average Treatment Effects with Application to Electronic Health Records Data

Understand

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 in estimating the ATE.