Covariate balance tests proposed for hidden confounding in offline RL

A new paper examines how covariate balance diagnostics, a tool borrowed from causal inference, can reveal hidden confounding or model misspecification when offline reinforcement learning is used to recommend treatments. The author argues these checks help assess whether learned treatment policies rest on valid assumptions. The work targets researchers applying RL to clinical or policy decision data.