Efficient and Robust Semi-supervised Estimation of ATE with Partially Annotated Treatment and Response

Understand

A notable challenge of leveraging Electronic Health Records (EHR) for treatment effect assessment is the lack of precise information on important clinical variables, including the treatment received and the response.

• Both treatment information and response often cannot be accurately captured by readily available EHR features and require labor intensive manual chart review to precisely annotate, which limits the number of available gold standard labels on these key variables.
• We consider average treatment effect (ATE) estimation under such a semi-supervised setting with a large number of unlabeled samples containing both confounders and imperfect EHR features for treatment and response.
• We derive the efficient influence function for ATE and use it to construct a semi-supervised multiple machine learning (SMMAL) estimator.