Post Reinforcement Learning Inference

Understand

We study estimation and inference using data collected by reinforcement learning (RL) algorithms.

• These algorithms adaptively experiment by interacting with individual units over multiple stages, updating their strategies based on past outcomes.
• Our goal is to evaluate a counterfactual policy after data collection and estimate structural parameters, such as dynamic treatment effects, that support credit assignment and quantify the impact of early actions on final outcomes.
• These parameters can often be defined as solutions to moment equations, motivating moment-based estimation methods developed for static data.