Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounders

TL;DR

This paper develops a robust fitted-Q-iteration method for offline reinforcement learning that accounts for unobserved confounders, providing theoretical guarantees and demonstrating effectiveness on healthcare data.

Contribution

It introduces an orthogonalized robust fitted-Q-iteration algorithm that handles unobserved confounders under a sensitivity model, with improved statistical properties and practical applicability.

Findings

Effective in simulations and healthcare data

Reduces dependence on quantile estimation error

Provides sample complexity bounds and theoretical insights

Abstract

Offline reinforcement learning is important in domains such as medicine, economics, and e-commerce where online experimentation is costly, dangerous or unethical, and where the true model is unknown. However, most methods assume all covariates used in the behavior policy's action decisions are observed. Though this assumption, sequential ignorability/unconfoundedness, likely does not hold in observational data, most of the data that accounts for selection into treatment may be observed, motivating sensitivity analysis. We study robust policy evaluation and policy optimization in the presence of sequentially-exogenous unobserved confounders under a sensitivity model. We propose and analyze orthogonalized robust fitted-Q-iteration that uses closed-form solutions of the robust Bellman operator to derive a loss minimization problem for the robust Q function, and adds a bias-correction to quantile estimation. Our algorithm enjoys the computational ease of fitted-Q-iteration and statistical improvements (reduced dependence on quantile estimation error) from orthogonalization. We provide sample complexity bounds, insights, and show effectiveness both in simulations and on real-world longitudinal healthcare data of treating sepsis. In particular, our model of sequential unobserved confounders yields an online Markov decision process, rather than partially observed Markov decision process: we illustrate how this can enable warm-starting optimistic reinforcement learning algorithms with valid robust bounds from observational data.