We study the problem of learning personalized decision policies from observational data while accounting for possible unobserved confounding in the data-generating process. 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 actually introduce significant harm rather then benefit due to overeager intervention when some unobserved confounding is present, as is actually the case in most applications dealing with observational data. Instead, we calibrate policy learning for realistic violations of this unverifiable assumption with uncertainty sets motivated by sensitivity analysis in causal inference. Our framework for confounding-robust policy improvement optimizes the minimax regret of a candidate policy against a baseline standard-of-care policy over an uncertainty set for propensity weights. We prove that if the uncertainty set is well-specified, our robust policy, when applied in practice, will do no worse than the baseline and improve upon it if possible. We characterize the adversarial optimization subproblem and use efficient algorithmic solutions to optimize over parametrized spaces of decision policies such as logistic treatment assignment and decision trees. We assess our methods on synthetic data and on a large clinical trial of acute ischaemic stroke treatment, demonstrating 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.
REF propose policy learning via minimax regret learning over uncertainty sets.
Confounding-Robust Policy Improvement
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