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Off-policy evaluation

Off-policy Evaluation (OPE), or offline evaluation in general, evaluates the performance of hypothetical policies leveraging only offline log data. It is particularly useful in applications where the online interaction involves high stakes and expensive setting such as precision medicine and recommender systems.

Papers

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Showing 211–220 of 265 papers

TitleStatusHype
Off-Policy Evaluation Using Information Borrowing and Context-Based SwitchingCode0
Causal Deepsets for Off-policy Evaluation under Spatial or Spatio-temporal InterferencesCode0
Counterfactual Mean EmbeddingsCode0
Doubly Robust Estimator for Off-Policy Evaluation with Large Action SpacesCode0
Doubly Robust Kernel Statistics for Testing Distributional Treatment EffectsCode0
Counterfactual Learning with Multioutput Deep KernelsCode0
Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision ProcessesCode0
Universal Off-Policy EvaluationCode0
State-Action Similarity-Based Representations for Off-Policy EvaluationCode0
Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision ProcessesCode0
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