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Individualized treatment rules (ITRs) are deterministic decision rules thatrecommend treatments to individuals based on their characteristics. Thoughubiquitous in medicine, ITRs are hardly ever evaluated in randomized controlledtrials. To evaluate ITRs from observational data, we introduce a newprobabilistic model and distinguish two situations: i) the situation of a newlydeveloped ITR, where data are from a population where no patient implements theITR, and ii) the situation of a partially implemented ITR, where data are froma population where the ITR is implemented in some unidentified patients. In theformer situation, we propose a procedure to explore the impact of an ITR undervarious implementation schemes. In the latter situation, on top of thefundamental problem of causal inference, we need to handle an additional latentvariable denoting implementation. To evaluate ITRs in this situation, wepropose an estimation procedure that relies on an expectation-maximizationalgorithm. In Monte Carlo simulations our estimators appear unbiased withconfidence intervals achieving nominal coverage. We illustrate our approach onthe MIMIC-III database, focusing on ITRs for dialysis initiation in patientswith acute kidney injury.

A Comprehensive Framework for the Evaluation of Individual Treatment Rules From Observational Data