A robust design selector minimizes worst-case planning risk over an ambiguity set of exposure mechanisms, with Wasserstein bounds and selector theorems, yielding different recommendations on public datasets.
Correlated cluster-based randomized exper- iments: Robust variance minimization.Management Science, 2023
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A support-aware offline decision framework for reserve-policy selection that outputs certified policies and shortlists instead of rankings, with a finite-catalog guarantee preserving the best supported policy.
A support-aware DSS integrates replay, OPE, lower-bound ranking, multi-sided guardrails, out-of-time validation, and interference-aware design to output launch-readiness classifications rather than single performance estimates, applied to RTB logs where a margin-gated floor policy is selected for va
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A robust design selector minimizes worst-case planning risk over an ambiguity set of exposure mechanisms, with Wasserstein bounds and selector theorems, yielding different recommendations on public datasets.
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A support-aware offline decision framework for reserve-policy selection that outputs certified policies and shortlists instead of rankings, with a finite-catalog guarantee preserving the best supported policy.