% Key papers for Differentiable and score-based DAG learning (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2017_permutation_56566,
  title = {{Permutation-based Causal Inference Algorithms with Interventions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9355},
}

@inproceedings{icml2018_characterizing_36569,
  title = {{Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2097},
}

@inproceedings{aaai2016_separators_105366,
  title = {{Separators and Adjustment Sets in Markov Equivalent DAGs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10424},
}

@inproceedings{aaai2023_reinforcement_4500,
  title = {{Reinforcement Causal Structure Learning on Order Graph}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26274},
}

@inproceedings{iclr2020_gradient_29858,
  title = {{Gradient-Based Neural DAG Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=rklbKA4YDS},
}

@inproceedings{aaai2019_counting_101556,
  title = {{Counting and Sampling from Markov Equivalent DAGs Using Clique Trees}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4249},
}

@inproceedings{icml2018_minimal_36850,
  title = {{Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2472},
}

@inproceedings{icml2020_lazyiter_38365,
  title = {{LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5920},
}

@inproceedings{neurips2023_bayesdag_69917,
  title = {{BayesDAG: Gradient-Based Posterior Inference for Causal Discovery}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/70043},
}

@inproceedings{iclr2022_granger_31659,
  title = {{Granger causal inference on DAGs identifies genomic loci regulating transcription}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6680},
}

@inproceedings{icml2021_integer_39472,
  title = {{Integer Programming for Causal Structure Learning in the Presence of Latent Variables}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10075},
}

@inproceedings{icml2020_causal_38006,
  title = {{Causal Structure Discovery from Distributions Arising from Mixtures of DAGs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6655},
}

@inproceedings{icml2024_stable_44066,
  title = {{Stable Differentiable Causal Discovery}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34386},
}

@inproceedings{neurips2019_an_57894,
  title = {{An Algorithm to Learn Polytree Networks with Hidden Nodes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14416},
}

@inproceedings{neurips2021_bcd_62865,
  title = {{BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28001},
}

@inproceedings{aaai2024_effective_5796,
  title = {{Effective Causal Discovery under Identifiable Heteroscedastic Noise Model}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29586},
}

@inproceedings{iclr2023_causal_32909,
  title = {{Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/10994},
}

@inproceedings{icml2020_characterizing_38011,
  title = {{Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6462},
}

@inproceedings{icml2023_discobax_42810,
  title = {{DiscoBAX - Discovery of optimal intervention sets in genomic experiment design}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/23977},
}

@inproceedings{neurips2021_multi_63626,
  title = {{Multi-task Learning of Order-Consistent Causal Graphs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27045},
}

@inproceedings{iclr2024_gene_34923,
  title = {{Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18151},
}

@inproceedings{neurips2024_markov_72307,
  title = {{Markov Equivalence and Consistency in Differentiable Structure Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95039},
}

@inproceedings{aaai2025_boosting_8278,
  title = {{Boosting Causal Structure Learning: An Asymmetric Exponential Modulation Gaussian-Based Adaptive Sample Reweighting Framework}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35470},
}

@inproceedings{icml2025_an_47322,
  title = {{An Efficient Search-and-Score Algorithm for Ancestral Graphs using Multivariate Information Scores for Complex Non-linear and Categorical Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45267},
}

@inproceedings{icml2026_pacer_50342,
  title = {{PACER: Acyclic Causal Discovery from Large-scale Interventional Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=BS8upx3Smw},
}

@inproceedings{icml2026_unveiling_51277,
  title = {{Unveiling the Structure of Do-Calculus Reasoning via Derivation Graphs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=KUBkuPwGf4},
}

@inproceedings{icml2026_interpretable_54250,
  title = {{Interpretable Neural ODEs for Gene Regulatory Network Discovery under Perturbations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=nG9bsQq2N6},
}

@inproceedings{neurips2024_qualitative_72147,
  title = {{Qualitative Mechanism Independence}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95189},
}

@inproceedings{neurips2025_dscs_74707,
  title = {{DSCS: Fast CPDAG-Based Verification of Collapsible Submodels in High-Dimensional Bayesian Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=0KnFWyzrbj},
}

@inproceedings{neurips2025_meta_77852,
  title = {{Meta-D2AG: Causal Graph Learning with Interventional Dynamic Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=YFZOIra4OE},
}

@inproceedings{neurips2025_relaxing_79491,
  title = {{Relaxing partition admissibility in Cluster-DAGs: a causal calculus with arbitrary variable clustering}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=pyDAihUMKV},
}

@inproceedings{iclr2025_causal_81022,
  title = {{Causal Discovery via Bayesian Optimization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30746},
}

@inproceedings{iclr2026_cats_87227,
  title = {{CaTs and DAGs: Integrating Directed Acyclic Graphs with Transformers for Causally Constrained Predictions}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=ZIQactmQxb},
}

@inproceedings{neurips2026_deep_95189,
  title = {{Deep Gaussian Processes on Directed Acyclic Graphs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/155757},
}

@inproceedings{aaai2026_sparse_107056,
  title = {{Sparse Additive Model Pruning for Order-Based Causal Structure Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39408},
}

@inproceedings{aaai2026_bayesian_108648,
  title = {{Bayesian Network Structural Consensus via Greedy Min-Cut Analysis}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/41000},
}

@inproceedings{aaai2024_a_5019,
  title = {{A Fixed-Parameter Tractable Algorithm for Counting Markov Equivalence Classes with the Same Skeleton}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30038},
}

@inproceedings{iclr2024_learning_34949,
  title = {{Learning Large DAGs is Harder than you Think: Many Losses are Minimal for the Wrong DAG}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18125},
}