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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 76–100 of 265 papers

TitleStatusHype
Causal Deepsets for Off-policy Evaluation under Spatial or Spatio-temporal InterferencesCode0
DOLCE: Decomposing Off-Policy Evaluation/Learning into Lagged and Current EffectsCode0
Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and LearningCode0
Deeply-Debiased Off-Policy Interval EstimationCode0
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment SettingsCode0
Abstract Reward Processes: Leveraging State Abstraction for Consistent Off-Policy EvaluationCode0
Human Choice Prediction in Language-based Persuasion Games: Simulation-based Off-Policy EvaluationCode0
From Importance Sampling to Doubly Robust Policy GradientCode0
Doubly Robust Kernel Statistics for Testing Distributional Treatment EffectsCode0
Future-Dependent Value-Based Off-Policy Evaluation in POMDPsCode0
Doubly robust off-policy evaluation with shrinkageCode0
A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision ProcessesCode0
Off-policy evaluation for slate recommendationCode0
Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision ProcessesCode0
RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health InterventionsCode0
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance WeightingCode0
Off-policy Evaluation with Deeply-abstracted StatesCode0
Hallucinated Adversarial Control for Conservative Offline Policy EvaluationCode0
Optimal and Adaptive Off-policy Evaluation in Contextual BanditsCode0
Importance Sampling Policy Evaluation with an Estimated Behavior PolicyCode0
Minimum Empirical Divergence for Sub-Gaussian Linear BanditsCode0
Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision ProcessesCode0
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided MarketsCode0
Balanced Off-Policy Evaluation for Personalized PricingCode0
Universal Off-Policy EvaluationCode0
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