RISE: Robust Individualized Decision Learning with Sensitive Variables
- URL: http://arxiv.org/abs/2211.06569v1
- Date: Sat, 12 Nov 2022 04:31:38 GMT
- Title: RISE: Robust Individualized Decision Learning with Sensitive Variables
- Authors: Xiaoqing Tan, Zhengling Qi, Christopher W. Seymour, Lu Tang
- Abstract summary: A naive baseline is to ignore sensitive variables in learning decision rules, leading to significant uncertainty and bias.
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.
- Score: 1.5293427903448025
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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