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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

Showing 2130 of 265 papers

TitleStatusHype
Off-Policy Evaluation via Adaptive Weighting with Data from Contextual BanditsCode1
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction EstimationCode1
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment SettingsCode0
RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health InterventionsCode0
Deeply-Debiased Off-Policy Interval EstimationCode0
Counterfactual Mean EmbeddingsCode0
Adaptive Estimator Selection for Off-Policy EvaluationCode0
Batch Stationary Distribution EstimationCode0
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal ModelsCode0
Balanced Off-Policy Evaluation for Personalized PricingCode0
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