% Key papers for Constraint-based structure learning with latents (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2017_discovering_12212,
  title = {{Discovering Causal Signals in Images}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{neurips2020_castle_59491,
  title = {{CASTLE: Regularization via Auxiliary Causal Graph Discovery}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17607},
}

@inproceedings{neurips2024_causal_70840,
  title = {{Causal-learn: Causal Discovery in Python}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/98316},
}

@inproceedings{aaai2016_on_105359,
  title = {{On Learning Causal Models from Relational Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10417},
}

@inproceedings{aaai2021_improving_758,
  title = {{Improving Causal Discovery By Optimal Bayesian Network Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/08741-improving-causal-discovery-by-optimal-bayesian-network-learning/},
}

@inproceedings{aaai2022_a_1672,
  title = {{A Hybrid Causal Structure Learning Algorithm for Mixed-Type Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/07435-a-hybrid-causal-structure-learning-algorithm-for-mixed-type-data/},
}

@inproceedings{neurips2017_learning_56414,
  title = {{Learning Causal Structures Using Regression Invariance}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9086},
}

@inproceedings{aaai2021_discovering_437,
  title = {{Discovering Fully Oriented Causal Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/08975-discovering-fully-oriented-causal-networks/},
}

@inproceedings{neurips2018_bayesian_56904,
  title = {{Bayesian Structure Learning by Recursive Bootstrap}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11995},
}

@inproceedings{aaai2019_recursively_101369,
  title = {{Recursively Learning Causal Structures Using Regression-Based Conditional Independence Test}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4170},
}

@inproceedings{neurips2021_beware_63465,
  title = {{Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy to Game}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26590},
}

@inproceedings{icml2019_learning_37481,
  title = {{Learning Hawkes Processes Under Synchronization Noise}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3756},
}

@inproceedings{neurips2020_bayesian_59438,
  title = {{Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17460},
}

@inproceedings{neurips2021_higher_61650,
  title = {{Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27239},
}

@inproceedings{aaai2024_tnpar_7483,
  title = {{TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequences}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30033},
}

@inproceedings{neurips2022_amortized_65562,
  title = {{Amortized Inference for Causal Structure Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/52935},
}

@inproceedings{aaai2020_reliable_103317,
  title = {{Reliable and Efficient Anytime Skeleton Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6569},
}

@inproceedings{aaai2022_efficient_2016,
  title = {{Efficient Causal Structure Learning from Multiple Interventional Datasets with Unknown Targets}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/08584-efficient-causal-structure-learning-from-multiple-interventional-datasets-with-unknown-targets/},
}

@inproceedings{aaai2023_information_3912,
  title = {{Information-Theoretic Causal Discovery and Intervention Detection over Multiple Environments}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26100},
}

@inproceedings{aaai2025_dcilp_8593,
  title = {{DCILP: A Distributed Approach for Large-Scale Causal Structure Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33795},
}

@inproceedings{neurips2024_tangent_71029,
  title = {{Tangent Space Causal Inference: Leveraging Vector Fields for Causal Discovery in Dynamical Systems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96180},
}

@inproceedings{icml2024_jacobian_43908,
  title = {{Jacobian Regularizer-based Neural Granger Causality}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34544},
}

@inproceedings{neurips2024_learning_70577,
  title = {{Learning Discrete Latent Variable Structures with Tensor Rank Conditions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96540},
}

@inproceedings{neurips2024_hybrid_74516,
  title = {{Hybrid Top-Down Global Causal Discovery with Local Search for Linear and Nonlinear Additive Noise Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93064},
}

@inproceedings{icml2024_optimal_43238,
  title = {{Optimal Transport for Structure Learning Under Missing Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35214},
}

@inproceedings{neurips2024_a_73741,
  title = {{A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93760},
}

@inproceedings{neurips2024_learning_73808,
  title = {{Learning the Latent Causal Structure for Modeling Label Noise}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93700},
}

@inproceedings{icml2025_since_45978,
  title = {{Since Faithfulness Fails: The Performance Limits of Neural Causal Discovery}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46568},
}

@inproceedings{icml2025_distinguishing_48137,
  title = {{Distinguishing Cause from Effect with Causal Velocity Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44472},
}

@inproceedings{icml2026_identifiable_51096,
  title = {{Identifiable Markov Switching Models with Instantaneous Effects and Exponential Families}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=IjrmQgIvHU},
}

@inproceedings{icml2026_causal_52724,
  title = {{Causal Preference Elicitation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=YTB6DpwLys},
}

@inproceedings{icml2026_a_53872,
  title = {{A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variables}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=jdbQzlnYll},
}

@inproceedings{icml2026_skewness_54164,
  title = {{Skewness-Robust Causal Discovery in Location-Scale Noise Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=mXn4ueFKnQ},
}

@inproceedings{neurips2024_qwo_71034,
  title = {{QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96175},
}

@inproceedings{neurips2024_ordering_71935,
  title = {{Ordering-Based Causal Discovery for Linear and Nonlinear Relations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95372},
}

@inproceedings{neurips2025_score_77459,
  title = {{Score-informed Neural Operator for Enhancing Ordering-based Causal Discovery}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=U5w9l0yQdo},
}

@inproceedings{neurips2025_causaldynamics_78261,
  title = {{CausalDynamics: A large‐scale benchmark for structural discovery of dynamical causal models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=ck7euvU7V3},
}