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Learning Robust Decision Policies from Observational Data
Muhammad Osama · Dave Zachariah · Peter Stoica

Tue Dec 08 09:00 AM -- 11:00 AM (PST) @ Poster Session 1 #191

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.

Author Information

Muhammad Osama (Uppsala University)
Dave Zachariah (Uppsala University)
Peter Stoica (Uppsala University)

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