% Key papers for Confounding-robust policy evaluation (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2019_dualdice_58173,
  title = {{DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14504},
}

@inproceedings{neurips2018_balanced_56888,
  title = {{Balanced Policy Evaluation and Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11849},
}

@inproceedings{iclr2019_woulda_28757,
  title = {{Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1076},
}

@inproceedings{neurips2018_confounding_56959,
  title = {{Confounding-Robust Policy Improvement}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11883},
}

@inproceedings{icml2018_importance_36745,
  title = {{Importance Weighted Transfer of Samples in Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2008},
}

@inproceedings{neurips2018_policy_57514,
  title = {{Policy Optimization via Importance Sampling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11531},
}

@inproceedings{neurips2022_markovian_64196,
  title = {{Markovian Interference in Experiments}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54464},
}

@inproceedings{icml2019_transfer_37821,
  title = {{Transfer of Samples in Policy Search via Multiple Importance Sampling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4094},
}

@inproceedings{aaai2021_action_76,
  title = {{Action Candidate Based Clipped Double Q-learning for Discrete and Continuous Action Tasks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/07979-action-candidate-based-clipped-double-q-learning-for-discrete-and-continuous-action-tasks/},
}

@inproceedings{icml2017_data_36127,
  title = {{Data-Efficient Policy Evaluation Through Behavior Policy Search}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/855},
}

@inproceedings{neurips2018_asymptotic_56877,
  title = {{Asymptotic optimality of adaptive importance sampling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11318},
}

@inproceedings{icml2019_batch_37175,
  title = {{Batch Policy Learning under Constraints}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3576},
}

@inproceedings{icml2019_optimistic_37633,
  title = {{Optimistic Policy Optimization via Multiple Importance Sampling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4049},
}

@inproceedings{aaai2019_on_100209,
  title = {{On Strength Adjustment for MCTS-Based Programs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/3917},
}

@inproceedings{aaai2017_importance_104555,
  title = {{Importance Sampling with Unequal Support}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10932},
}

@inproceedings{aaai2022_lifelong_2363,
  title = {{Lifelong Hyper-Policy Optimization with Multiple Importance Sampling Regularization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/07525-lifelong-hyper-policy-optimization-with-multiple-importance-sampling-regularization/},
}

@inproceedings{icml2020_efficient_38148,
  title = {{Efficient Policy Learning from Surrogate-Loss Classification Reductions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6218},
}

@inproceedings{neurips2019_policy_58809,
  title = {{Policy Evaluation with Latent Confounders via Optimal Balance}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13629},
}

@inproceedings{aaai2023_integrating_3917,
  title = {{Integrating Reward Maximization and Population Estimation: Sequential Decision-Making for Internal Revenue Service Audit Selection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25637},
}

@inproceedings{icml2019_dimension_37290,
  title = {{Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3671},
}

@inproceedings{aaai2026_dimm_106636,
  title = {{DIMM: Decoupled Multi-hierarchy Kalman Filter via Reinforcement Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/38943},
}

@inproceedings{aaai2025_distributionally_8774,
  title = {{Distributionally Robust Policy Evaluation and Learning for Continuous Treatment with Observational Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34003},
}

@inproceedings{aaai2025_efficient_8912,
  title = {{Efficient Multi-Policy Evaluation for Reinforcement Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34086},
}

@inproceedings{icml2024_policy_43568,
  title = {{Policy Learning for Balancing Short-Term and Long-Term Rewards}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34884},
}

@inproceedings{icml2024_combining_45317,
  title = {{Combining Experimental and Historical Data for Policy Evaluation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33135},
}

@inproceedings{icml2025_distributionally_47422,
  title = {{Distributionally Robust Policy Learning under Concept Drifts}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45171},
}

@inproceedings{icml2026_gipo_51807,
  title = {{GIPO: Gaussian Importance Sampling Policy Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Q5DHzFVOxf},
}

@inproceedings{neurips2024_externally_70303,
  title = {{Externally Valid Policy Evaluation from Randomized Trials Using Additional Observational Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96790},
}

@inproceedings{neurips2024_efficient_72006,
  title = {{Efficient Policy Evaluation Across Multiple Different Experimental Datasets}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95308},
}

@inproceedings{neurips2025_pessimistic_75860,
  title = {{Pessimistic Data Integration for Policy Evaluation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Ct60KFYMIf},
}

@inproceedings{iclr2025_doubly_80851,
  title = {{Doubly Optimal Policy Evaluation for Reinforcement Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30916},
}

@inproceedings{iclr2026_efficient_84884,
  title = {{Efficient and Sharp Off-Policy Learning under Unobserved Confounding}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=7nTKiJLkWS},
}

@inproceedings{neurips2026_risk_95217,
  title = {{Risk-Controlled Post-Processing of Decision Policies}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/154732},
}

@inproceedings{icml2025_unraveling_47777,
  title = {{Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experiments}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44823},
}

@inproceedings{icml2026_a_49357,
  title = {{A Minimax Approach for Optimal Intervention Policy Learning with Two-Stage Outcomes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=1lKO23WFt8},
}

@inproceedings{icml2026_personalized_49834,
  title = {{Personalized Policy Learning through Discrete Experimentation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=698yKwEXf0},
}

@inproceedings{neurips2026_beyond_89686,
  title = {{Beyond IPS: Reliable Counterfactual Evaluation in Multi-Stage Ad Systems without Logged Propensities}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/151796},
}

@inproceedings{neurips2026_distributionally_95445,
  title = {{Distributionally-Robust Policy Learning from Observational Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/151620},
}