The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records

TL;DR

This paper introduces the medical deconfounder, a machine learning method that estimates unbiased treatment effects from electronic health records by modeling medication prescriptions to account for unobserved confounders.

Contribution

The medical deconfounder is a novel algorithm that captures unobserved confounders using medication prescription data, improving treatment effect estimation from EHRs.

Findings

Produces more accurate treatment effect estimates than classical methods.

Identifies effective medications consistent with medical literature.

Validated on simulated and real datasets.

Abstract

The treatment effects of medications play a key role in guiding medical prescriptions. They are usually assessed with randomized controlled trials (RCTs), which are expensive. Recently, large-scale electronic health records (EHRs) have become available, opening up new opportunities for more cost-effective assessments. However, assessing a treatment effect from EHRs is challenging: it is biased by unobserved confounders, unmeasured variables that affect both patients' medical prescription and their outcome, e.g. the patients' social economic status. To adjust for unobserved confounders, we develop the medical deconfounder, a machine learning algorithm that unbiasedly estimates treatment effects from EHRs. The medical deconfounder first constructs a substitute confounder by modeling which medications were prescribed to each patient; this substitute confounder is guaranteed to capture all multi-medication confounders, observed or unobserved (arXiv:1805.06826). It then uses this substitute confounder to adjust for the confounding bias in the analysis. We validate the medical deconfounder on two simulated and two real medical data sets. Compared to classical approaches, the medical deconfounder produces closer-to-truth treatment effect estimates; it also identifies effective medications that are more consistent with the findings in the medical literature.