Tao of RWD Blog

An introduction to the principle from offline reinforcement learning and how it can be applied to dynamic treatment regime estimation to produce treatment recommendations that account for model uncertainty using a Bayesian learning approach.

This article introduces dynamic treatment regimes (DTRs) as a bridge between causal inference and reinforcement learning (RL), showing how sequential clinical decisions can be optimized using Q-learning and backward induction while acknowledging the challenge of model unreliability with sparse data.