An identification theorem shows that a randomized experiment and simulator together recover causal model values from confounded logs, with logs used only afterward to reduce estimation error.
Doubly robust policy evaluation and optimization
2 Pith papers cite this work. Polarity classification is still indexing.
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CASP selects lower-burden two-stage recommender policies by combining doubly robust estimation with a penalty for weak data support and provides theoretical guarantees for conservative selection.
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The Partial Testimony of Logs: Evaluation of Language Model Generation under Confounded Model Choice
An identification theorem shows that a randomized experiment and simulator together recover causal model values from confounded logs, with logs used only afterward to reduce estimation error.
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CASP: Support-Aware Offline Policy Selection for Two-Stage Recommender Systems
CASP selects lower-burden two-stage recommender policies by combining doubly robust estimation with a penalty for weak data support and provides theoretical guarantees for conservative selection.