% Key papers for Meta-learners and partial identification (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{aaai2021_treatment_1557,
  title = {{Treatment Effect Estimation with Disentangled Latent Factors}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/10923-treatment-effect-estimation-with-disentangled-latent-factors/},
}

@inproceedings{neurips2019_machine_58567,
  title = {{Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14635},
}

@inproceedings{neurips2021_on_61819,
  title = {{On Inductive Biases for Heterogeneous Treatment Effect Estimation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27463},
}

@inproceedings{neurips2021_synctwin_61337,
  title = {{SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27560},
}

@inproceedings{icml2022_a_40187,
  title = {{A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16611},
}

@inproceedings{neurips2021_really_61741,
  title = {{Really Doing Great at Estimating CATE? A Critical Look at ML Benchmarking Practices in Treatment Effect Estimation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://openreview.net/forum?id=FQLzQqGEAH},
}

@inproceedings{neurips2023_optimal_67338,
  title = {{Optimal Transport for Treatment Effect Estimation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72388},
}

@inproceedings{neurips2020_minimax_60323,
  title = {{Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment Effects}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18109},
}

@inproceedings{neurips2019_debiased_58090,
  title = {{Debiased Bayesian inference for average treatment effects}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14165},
}

@inproceedings{neurips2021_learning_62853,
  title = {{Learning Treatment Effects in Panels with General Intervention Patterns}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27892},
}

@inproceedings{neurips2021_estimating_63186,
  title = {{Estimating Multi-cause Treatment Effects via Single-cause Perturbation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27659},
}

@inproceedings{icml2021_budgeted_39114,
  title = {{Budgeted Heterogeneous Treatment Effect Estimation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10267},
}

@inproceedings{aaai2024_exploiting_5906,
  title = {{Exploiting Geometry for Treatment Effect Estimation via Optimal Transport}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29564},
}

@inproceedings{iclr2023_estimating_32661,
  title = {{Estimating individual treatment effects under unobserved confounding using binary instruments}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/12055},
}

@inproceedings{icml2021_permutation_39781,
  title = {{Permutation Weighting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9133},
}

@inproceedings{neurips2022_sample_63872,
  title = {{Sample Constrained Treatment Effect Estimation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53615},
}

@inproceedings{aaai2024_uncertainty_7677,
  title = {{Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30025},
}

@inproceedings{iclr2024_bounds_34796,
  title = {{Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18278},
}

@inproceedings{icml2023_proximal_41909,
  title = {{Proximal Causal Learning of Conditional Average Treatment Effects}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24450},
}

@inproceedings{icml2023_b_42775,
  title = {{B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24427},
}

@inproceedings{neurips2024_higher_70398,
  title = {{Higher-Order Causal Message Passing for Experimentation with Complex Interference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96704},
}

@inproceedings{neurips2024_hierarchical_70524,
  title = {{Hierarchical and Density-based Causal Clustering}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96587},
}

@inproceedings{neurips2024_a_72958,
  title = {{A Non-parametric Direct Learning Approach to Heterogeneous Treatment Effect Estimation under Unmeasured Confounding}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94455},
}

@inproceedings{neurips2024_estimating_72964,
  title = {{Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94449},
}

@inproceedings{aaai2025_conformal_8500,
  title = {{Conformal Inference of Individual Treatment Effects Using Conditional Density Estimates}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35397},
}

@inproceedings{iclr2024_adaptive_33618,
  title = {{Adaptive Instrument Design for Indirect Experiments}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19457},
}

@inproceedings{iclr2024_empirical_35675,
  title = {{Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17398},
}

@inproceedings{icml2024_continuous_44864,
  title = {{Continuous Treatment Effects with Surrogate Outcomes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33588},
}

@inproceedings{icml2024_meta_45526,
  title = {{Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32926},
}

@inproceedings{icml2025_optimistic_45905,
  title = {{Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46638},
}

@inproceedings{icml2025_measuring_46809,
  title = {{Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45766},
}

@inproceedings{icml2025_heterogeneous_47174,
  title = {{Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45413},
}

@inproceedings{icml2025_a_47895,
  title = {{A Meta-learner for Heterogeneous Effects in Difference-in-Differences}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44708},
}

@inproceedings{icml2025_learning_48635,
  title = {{Learning Representations of Instruments for Partial Identification of Treatment Effects}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43986},
}

@inproceedings{icml2025_doubly_48942,
  title = {{Doubly Protected Estimation for Survival Outcomes Utilizing External Controls for Randomized Clinical Trials}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43681},
}

@inproceedings{icml2026_conditional_51387,
  title = {{Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=LfUXGnwVzw},
}

@inproceedings{icml2026_budgeted_51580,
  title = {{Budgeted Active Experimentation for Treatment Effect Estimation from Observational and Randomized Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Np7Y3AEVNq},
}