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Arxiv

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Image Credit: Arxiv

Distributionally Robust Policy Learning under Concept Drifts

  • Distributionally robust policy learning focuses on finding a policy that performs well even under the worst-case distributional shift.
  • Existing methods consider the worst-case joint distribution, which can be overly conservative.
  • This study addresses robust policy learning under concept drifts, where the conditional relationship between outcome and covariate changes.
  • Proposed methods include a doubly-robust estimator and a learning algorithm for maximizing policy value, showing improvement over existing benchmarks.

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