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arXiv · 2006.02355

Learning Robust Decision Policies from Observational Data

Muhammad Osama·Dave Zachariah·Peter Stoica
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Abstract

We address the problem of learning a decision policy from observational data of past decisions in contexts with features and associated outcomes. The past policy maybe unknown and in safety-critical applications, such as medical decision support, it is of interest to learn robust policies that reduce the risk of outcomes with high costs. In this paper, we develop a method for learning policies that reduce tails of the cost distribution at a specified level and, moreover, provide a statistically valid bound on the cost of each decision. These properties are valid under finite samples -- even in scenarios with uneven or no overlap between features for different decisions in the observed data -- by building on recent results in conformal prediction. The performance and statistical properties of the proposed method are illustrated using both real and synthetic data.

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Muhammad Osama, Dave Zachariah, Peter Stoica. 2020-06-03. Learning Robust Decision Policies from Observational Data. https://arxiv.org/abs/2006.02355

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