Establishes finite-sample regret bounds of order sqrt(N-dim(Π)/N) for IPW and DR estimators in Wasserstein policy learning with distributional outcomes, plus a matching minimax lower bound.
arXiv preprint arXiv:2503.05024 , year=
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Bayesian shrinkage priors on factor models produce sparse substitute confounders that support consistent regression-adjusted causal estimates under latent variable identification assumptions.
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Wasserstein Policy Learning for Distributional Outcomes
Establishes finite-sample regret bounds of order sqrt(N-dim(Π)/N) for IPW and DR estimators in Wasserstein policy learning with distributional outcomes, plus a matching minimax lower bound.
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Shrinkage priors for Bayesian Substitute Confounders
Bayesian shrinkage priors on factor models produce sparse substitute confounders that support consistent regression-adjusted causal estimates under latent variable identification assumptions.