% Key papers for Benchmarking treatment effect models (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2018_ganite_28463,
  title = {{GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/153},
}

@inproceedings{neurips2017_bayesian_56177,
  title = {{Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9126},
}

@inproceedings{neurips2018_representation_57572,
  title = {{Representation Learning for Treatment Effect Estimation from Observational Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11271},
}

@inproceedings{icml2017_estimating_36180,
  title = {{Estimating individual treatment effect: generalization bounds and algorithms}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/790},
}

@inproceedings{aaai2020_learning_102152,
  title = {{Learning Counterfactual Representations for Estimating Individual Dose-Response Curves}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6014},
}

@inproceedings{icml2018_limits_36825,
  title = {{Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2226},
}

@inproceedings{neurips2019_adapting_57865,
  title = {{Adapting Neural Networks for the Estimation of Treatment Effects}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13443},
}

@inproceedings{aaai2018_deep_105732,
  title = {{Deep-Treat: Learning Optimal Personalized Treatments From Observational Data Using Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11841},
}

@inproceedings{iclr2020_learning_29528,
  title = {{Learning Disentangled Representations for CounterFactual Regression}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=HkxBJT4YvB},
}

@inproceedings{neurips2017_matching_56466,
  title = {{Matching on Balanced Nonlinear Representations for Treatment Effects Estimation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8887},
}

@inproceedings{iclr2020_estimating_29288,
  title = {{Estimating counterfactual treatment outcomes over time through adversarially balanced representations}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=BJg866NFvB},
}

@inproceedings{aaai2017_informative_104423,
  title = {{Informative Subspace Learning for Counterfactual Inference}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10800},
}

@inproceedings{icml2017_recursive_36361,
  title = {{Recursive Partitioning for Personalization using Observational Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/818},
}

@inproceedings{neurips2020_counterfactual_59614,
  title = {{Counterfactual Prediction for Bundle Treatment}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18330},
}

@inproceedings{icml2019_active_37124,
  title = {{Active Learning for Decision-Making from Imbalanced Observational Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4171},
}

@inproceedings{aaai2019_learning_101738,
  title = {{Learning Triggers for Heterogeneous Treatment Effects}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4453},
}

@inproceedings{neurips2020_organite_60549,
  title = {{OrganITE: Optimal transplant donor organ offering using an individual treatment effect}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17797},
}

@inproceedings{neurips2019_learning_58506,
  title = {{Learning Sample-Specific Models with Low-Rank Personalized Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13527},
}

@inproceedings{aaai2022_imbalance_2226,
  title = {{Imbalance-Aware Uplift Modeling for Observational Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/06313-imbalance-aware-uplift-modeling-for-observational-data/},
}

@inproceedings{neurips2021_causal_62880,
  title = {{Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26895},
}

@inproceedings{aaai2024_contrastive_5496,
  title = {{Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29663},
}

@inproceedings{aaai2024_continuous_5493,
  title = {{Continuous Treatment Effect Estimation Using Gradient Interpolation and Kernel Smoothing}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29353},
}

@inproceedings{aaai2024_adversarially_5151,
  title = {{Adversarially Balanced Representation for Continuous Treatment Effect Estimation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29207},
}

@inproceedings{aaai2024_kg_6370,
  title = {{KG-TREAT: Pre-training for Treatment Effect Estimation by Synergizing Patient Data with Knowledge Graphs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28727},
}

@inproceedings{neurips2025_llm_79783,
  title = {{LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=sv41aaGTit},
}

@inproceedings{aaai2026_auxiliary_104330,
  title = {{Auxiliary Gene Learning: Spatial Gene Expression Estimation by Auxiliary Gene Selection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/37857},
}

@inproceedings{iclr2024_defining_33493,
  title = {{Defining Expertise: Applications to Treatment Effect Estimation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19582},
}

@inproceedings{iclr2024_ode_35314,
  title = {{ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17759},
}

@inproceedings{iclr2024_bayesian_35515,
  title = {{Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17558},
}

@inproceedings{icml2024_discret_43710,
  title = {{DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34742},
}

@inproceedings{icml2024_longitudinal_43762,
  title = {{Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34690},
}

@inproceedings{icml2024_pairnet_45354,
  title = {{PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33097},
}

@inproceedings{icml2025_enhancing_46994,
  title = {{Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45589},
}

@inproceedings{icml2025_autocate_47256,
  title = {{AutoCATE: End-to-End, Automated Treatment Effect Estimation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45333},
}

@inproceedings{icml2026_deepblip_51085,
  title = {{DeepBlip: Estimating Conditional Average Treatment Effects Over Time}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Ig36S9zALV},
}

@inproceedings{icml2026_rank_55291,
  title = {{Rank-Learner: Orthogonal Ranking of Treatment Effects}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=xM56OFSUaN},
}

@inproceedings{icml2026_estimating_55498,
  title = {{Estimating Continuous Treatment Effects with Two-Stage Kernel Ridge Regression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=ziqS4yXFQX},
}

@inproceedings{neurips2024_dynamic_70447,
  title = {{Dynamic Subgroup Identification in Covariate-adjusted Response-adaptive Randomization Experiments}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96660},
}

@inproceedings{neurips2024_peace_73070,
  title = {{PEACE: A Dataset of Pharmaceutical Care for Cancer Pain Analgesia Evaluation and Medication Decision}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97596},
}

@inproceedings{neurips2025_orthogonal_76011,
  title = {{Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=EdP45Yxdc3},
}