machine learning

Evaluating covariate balance for long time horizon Markov decision processes

arXiv:2607.15080

summary

The paper investigates how covariate balance diagnostics can be used to detect hidden confounding and model miss‑specification in offline reinforcement learning for long‑horizon treatment recommendation problems, finding current methods may be insufficient for ensuring statistical robustness.

Abstract

This article explores the application of covariate balance diagnostics for detecting the presence of hidden confounding/model miss-specification in studies applying offline reinforcement learning (RL) to deriving optimal treatment recommendations. The results demonstrate that, either there is a high risk of bias within existing offline RL studies for treatment recommendations or, existing covariate balance metrics are not sufficient to assess such studies. Regardless, existing offline RL studies cannot be concluded as being statistically robust. The conclusions propose future research directions for obtaining more methodologically robust applications of offline RL to treatment recommendation problems.

Topics & keywords

#offline reinforcement learning#covariate balance#causal inference#treatment recommendation#markov decision processes#bias detectioncovariate balance diagnosticsoffline RLhidden confoundingmodel misspecificationlong-horizon MDPtreatment recommendation