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

@inproceedings{icml2016_learning_35885,
  title = {{Learning Representations for Counterfactual Inference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/johansson16.html},
}

@inproceedings{neurips2020_algorithmic_59359,
  title = {{Algorithmic recourse under imperfect causal knowledge: a probabilistic approach}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17718},
}

@inproceedings{neurips2020_deep_59665,
  title = {{Deep Structural Causal Models for Tractable Counterfactual Inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17365},
}

@inproceedings{aaai2019_abstracting_101317,
  title = {{Abstracting Causal Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4117},
}

@inproceedings{aaai2018_towards_101473,
  title = {{Towards Formal Definitions of Blameworthiness, Intention, and Moral Responsibility}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11557},
}

@inproceedings{neurips2020_asymmetric_59392,
  title = {{Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17898},
}

@inproceedings{aaai2022_debiasing_1924,
  title = {{Debiasing NLU Models via Causal Intervention and Counterfactual Reasoning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/11376-debiasing-nlu-models-via-causal-intervention-and-counterfactual-reasoning/},
}

@inproceedings{neurips2020_causal_59519,
  title = {{Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18809},
}

@inproceedings{aaai2018_situation_101477,
  title = {{Situation Calculus Semantics for Actual Causality}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11561},
}

@inproceedings{neurips2018_structural_57661,
  title = {{Structural Causal Bandits: Where to Intervene?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11265},
}

@inproceedings{neurips2021_a_63014,
  title = {{A Causal Lens for Controllable Text Generation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26438},
}

@inproceedings{aaai2020_probabilistic_103325,
  title = {{Probabilistic Reasoning Across the Causal Hierarchy}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6577},
}

@inproceedings{neurips2021_the_62890,
  title = {{The Causal-Neural Connection: Expressiveness, Learnability, and Inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26317},
}

@inproceedings{aaai2022_reasoning_2615,
  title = {{Reasoning about Causal Models with Infinitely Many Variables}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/05668-reasoning-about-causal-models-with-infinitely-many-variables/},
}

@inproceedings{aaai2022_unsupervised_2920,
  title = {{Unsupervised Editing for Counterfactual Stories}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/10473-unsupervised-editing-for-counterfactual-stories/},
}

@inproceedings{icml2022_partial_40993,
  title = {{Partial Counterfactual Identification from Observational and Experimental Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17031},
}

@inproceedings{aaai2018_rational_101474,
  title = {{Rational Inference Patterns Based on Conditional Logic}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11558},
}

@inproceedings{iclr2021_debiasing_30021,
  title = {{Debiasing Concept-based Explanations with Causal Analysis}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2732},
}

@inproceedings{neurips2021_nested_63596,
  title = {{Nested Counterfactual Identification from Arbitrary Surrogate Experiments}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26670},
}

@inproceedings{icml2020_full_38236,
  title = {{Full Law Identification in Graphical Models of Missing Data: Completeness Results}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5983},
}

@inproceedings{iclr2025_causal_81097,
  title = {{Causal Order: The Key to Leveraging Imperfect Experts in Causal Inference}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30671},
}

@inproceedings{aaai2026_ktcf_108864,
  title = {{KTCF: Actionable Recourse in Knowledge Tracing via Counterfactual Explanations for Education}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/41216},
}

@inproceedings{aaai2025_sequential_10747,
  title = {{Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34131},
}

@inproceedings{neurips2024_a_73747,
  title = {{A Compositional Atlas for Algebraic Circuits}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93754},
}

@inproceedings{iclr2025_from_83655,
  title = {{From Probability to Counterfactuals: the Increasing Complexity of Satisfiability in Pearl's Causal Hierarchy}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28162},
}

@inproceedings{neurips2026_controllable_93466,
  title = {{Controllable Generative Sandbox for Causal Inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/155513},
}

@inproceedings{aaai2025_a_7941,
  title = {{A Variable Occurrence-Centric Framework for Inconsistency Handling}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33661},
}

@inproceedings{aaai2025_causally_8400,
  title = {{Causally Consistent Normalizing Flow}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34460},
}

@inproceedings{aaai2025_counterfactual_8549,
  title = {{Counterfactual Identification Under Monotonicity Constraints}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34888},
}

@inproceedings{aaai2025_temporal_10938,
  title = {{Temporal Causal Reasoning with (Non-Recursive) Structural Equation Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33639},
}

@inproceedings{iclr2024_towards_35519,
  title = {{Towards Characterizing Domain Counterfactuals for Invertible Latent Causal Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17554},
}

@inproceedings{icml2024_a_44087,
  title = {{A Fixed-Point Approach for Causal Generative Modeling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34365},
}

@inproceedings{icml2024_position_44886,
  title = {{Position: The Causal Revolution Needs Scientific Pragmatism}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33566},
}

@inproceedings{icml2025_a_46224,
  title = {{A New Approach to Backtracking Counterfactual Explanations: A Unified Causal Framework for Efficient Model Interpretability}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46336},
}

@inproceedings{icml2025_exogenous_46413,
  title = {{Exogenous Isomorphism for Counterfactual Identifiability}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46152},
}

@inproceedings{icml2026_exactly_51761,
  title = {{Exactly Computing do-Shapley Values}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Peim0KY6ty},
}

@inproceedings{icml2026_formalizing_51890,
  title = {{Formalizing and Falsifying Causal Pathways of Rare Events}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=QoInle2UdN},
}

@inproceedings{icml2026_gateways_52136,
  title = {{Gateways to Tractability for Satisfiability in Pearl’s Causal Hierarchy}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=T5Oi5wodZT},
}

@inproceedings{icml2026_relational_52479,
  title = {{Relational Structural Causal Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=WTBaZHtIra},
}

@inproceedings{icml2026_causal_53873,
  title = {{Causal Identification from Counterfactual Data: Completeness and Bounding Results}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=jeCyxur8XB},
}