AI & Machine Learning

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

Ali NematiJan 1, 2025

25 sec read91 views0 listens

Researchers developed a semi-supervised multiple machine learning (SMMAL) estimator to accurately assess average treatment effects from Electronic Health Records (EHR), even when precise treatment and outcome data are limited. This method enhances efficiency by using noisy surrogate data for treatment and response, making it particularly useful for content creators and researchers aiming to leverage EHRs with minimal labeled data.

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