A0178
Title: RISE: Robust individualized decision learning with sensitive variables
Authors: Lu Tang - University of Pittsburgh (United States) [presenting]
Abstract: RISE, a robust, individualized decision learning framework with sensitive variables, is introduced, where sensitive variables are collectable data essential to the intervention decision. However, their inclusion in decision-making is prohibited due to reasons such as delayed availability or fairness concerns. A naive baseline is to ignore these sensitive variables in learning decision rules, leading to significant uncertainty and bias. To address this, a decision learning framework is proposed to incorporate sensitive variables during offline training but not include them in the input of the learned decision rule during model deployment. Specifically, from a causal perspective, the proposed framework intends to improve the worst-case outcomes of individuals caused by sensitive variables unavailable at the time of decision. Unlike most existing literature that uses mean-optimal objectives, a robust learning framework is proposed by finding a newly defined quantile-or infimum-optimal decision rule. The reliable performance of the proposed method is demonstrated through synthetic experiments and three real-world applications.
