Distributionally-Robust Policy Learning with Observational Data

Key points

• We study distributionally-robust policy learning: given observational data, we seek a policy $π\in Π$ that maximizes the worst-case policy value over an $f$-divergence ball of radius $ρ$ around the data-generating distribution.1
• We propose an estimator built on cross-fitted doubly-robust scores for the conditional treatment effect, paired with a DRO solver that handles KL, $χ^2$, and CVaR ambiguity sets.1
• First, the estimated policy attains the standard $\widetilde{O}(n^{-1/2})$ rate on its DRO regret, and inherits the doubly-robust property -- the rate continues to hold whenever either the outcome model or the propensity model is consistently estimated, even at slow nonparametric rates.1
• To our knowledge, this is the first work to incorporate a margin condition into the analysis of distributionally-robust policy learning, and the first to obtain fast rates in this setting.1

Sources (1)

• [1]Distributionally-Robust Policy Learning with Observational Data
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 11:39 PM

  Policy learning from observational data is fundamental to applications ranging from personalized medicine to welfare-program targeting, but standard approaches optimize empirical performance and may fail when the deployment distribution differs from the training distribution.

  We study distributionally-robust policy learning: given observational data, we seek a policy $π\in Π$ that maximizes the worst-case policy value over an $f$-divergence ball of radius $ρ$ around the data-generating distribution.

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