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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 191200 of 265 papers

TitleStatusHype
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal ModelsCode0
Cross-Validated Off-Policy EvaluationCode0
Semi-Parametric Efficient Policy Learning with Continuous ActionsCode0
Predictive Performance Comparison of Decision Policies Under ConfoundingCode0
RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health InterventionsCode0
Adaptive Estimator Selection for Off-Policy EvaluationCode0
A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision ProcessesCode0
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment SettingsCode0
Deeply-Debiased Off-Policy Interval EstimationCode0
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy EvaluationCode0
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