Confounding-Robust Policy Improvement

Abstract

We study the problem of learning personalized decision policies from observational data while accounting for possible unobserved confounding. Previous approaches, which assume unconfoundedness, i.e., that no unobserved confounders affect both the treatment assignment as well as outcome, can lead to policies that introduce harm rather than benefit when some unobserved confounding is present, as is generally the case with observational data. Instead, since policy value and regret may not be point-identifiable, we study a method that minimizes the worst-case estimated regret of a candidate policy against a baseline policy over an uncertainty set for propensity weights that controls the extent of unobserved confounding. We prove generalization guarantees that ensure our policy will be safe when applied in practice and will in fact obtain the best-possible uniform control on the range of all possible population regrets that agree with the possible extent of confounding. We develop efficient algorithmic solutions to compute this confounding-robust policy. Finally, we assess and compare our methods on synthetic and semi-synthetic data. In particular, we consider a case study on personalizing hormone replacement therapy based on observational data, where we validate our results on a randomized experiment. We demonstrate that hidden confounding can hinder existing policy learning approaches and lead to unwarranted harm, while our robust approach guarantees safety and focuses on well-evidenced improvement, a necessity for making personalized treatment policies learned from observational data reliable in practice.

References in corpus (5)

• Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning
• Balanced Policy Evaluation and Learning
• Safe Policy Improvement by Minimizing Robust Baseline Regret
• Policy Evaluation and Optimization with Continuous Treatments
• Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap

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• Sequential Batch Learning in Finite-Action Linear Contextual Bandits
• Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination
• Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning
• Treatment Allocation under Uncertain Costs
• Dynamic Measurement Scheduling for Event Forecasting using Deep RL
• Dynamic Batch Learning in High-Dimensional Sparse Linear Contextual Bandits
• Statistically Efficient Off-Policy Policy Gradients
• Counterfactual Prediction Under Selective Confounding