RISE: Robust Individualized Decision Learning with Sensitive Variables
This addresses fairness and uncertainty issues in decision-making for applications like healthcare or policy, though it is incremental as it builds on existing causal and robust learning methods.
The paper tackles the problem of learning individualized decision rules when sensitive variables are available during training but prohibited at deployment, by proposing a robust framework that improves worst-case outcomes using quantile- or infimum-optimal objectives, with performance validated through synthetic and real-world experiments.
This paper introduces RISE, a robust individualized decision learning framework with sensitive variables, where sensitive variables are collectible data and important to the intervention decision, but 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, we propose a decision learning framework 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 that are unavailable at the time of decision. Unlike most existing literature that uses mean-optimal objectives, we propose a robust learning framework 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.