Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework
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Publication:6077592
DOI10.1080/01621459.2022.2027776arXiv2002.01711OpenAlexW4205913461WikidataQ114898045 ScholiaQ114898045MaRDI QIDQ6077592
Shikai Luo, Hong-Tu Zhu, Jieping Ye, Rui Song, Chengchun Shi, Xiao-yu Wang
Publication date: 18 October 2023
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2002.01711
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Cites Work
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- Performance guarantees for individualized treatment rules
- High-dimensional \(A\)-learning for optimal dynamic treatment regimes
- Information-regret compromise in covariate-adaptive treatment allocation
- Bayesian method for causal inference in spatially-correlated multivariate time series
- Jackknife, bootstrap and other resampling methods in regression analysis
- The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
- Asymptotic properties of covariate-adjusted response-adaptive designs
- Penalized Q-learning for dynamic treatment regimens
- Interpretable Dynamic Treatment Regimes
- Discrete Sequential Boundaries for Clinical Trials
- Toward Causal Inference With Interference
- Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
- Quantile-Optimal Treatment Regimes
- Estimating Individualized Treatment Rules Using Outcome Weighted Learning
- Optimal Dynamic Treatment Regimes
- A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
- Maximin Projection Learning for Optimal Treatment Decision with Heterogeneous Individualized Treatment Effects
- Learning Optimal Distributionally Robust Individualized Treatment Rules
- Estimating Dynamic Treatment Regimes in Mobile Health Using V-Learning
- Randomization Inference for Peer Effects
- Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading
- Testing for arbitrary interference on experimentation platforms
- Causal Inference for Statistics, Social, and Biomedical Sciences
- Inference for non-regular parameters in optimal dynamic treatment regimes
- Greedy outcome weighted tree learning of optimal personalized treatment rules
- Optimal Structural Nested Models for Optimal Sequential Decisions
- A Unified Family of Covariate-Adjusted Response-Adaptive Designs Based on Efficiency and Ethics
- New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes
- Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions
- Personalized Policy Learning Using Longitudinal Mobile Health Data
- Synthetic learner: model-free inference on treatments over time
