% Key papers for Identifiability with hidden confounders (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{aaai2017_causal_101447,
  title = {{Causal Discovery Using Regression-Based Conditional Independence Tests}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10698},
}

@inproceedings{neurips2022_root_66366,
  title = {{Root Cause Analysis of Failures in Microservices through Causal Discovery}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/52853},
}

@inproceedings{neurips2018_causal_56936,
  title = {{Causal Inference via Kernel Deviance Measures}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11673},
}

@inproceedings{neurips2018_causal_56934,
  title = {{Causal Discovery from Discrete Data using Hidden Compact Representation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11274},
}

@inproceedings{aaai2018_measuring_101471,
  title = {{Measuring Conditional Independence by Independent Residuals: Theoretical Results and Application in Causal Discovery}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11555},
}

@inproceedings{neurips2016_ancestral_55579,
  title = {{Ancestral Causal Inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7114},
}

@inproceedings{aaai2021_testing_1484,
  title = {{Testing Independence Between Linear Combinations for Causal Discovery}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/06538-testing-independence-between-linear-combinations-for-causal-discovery/},
}

@inproceedings{aaai2022_residual_2639,
  title = {{Residual Similarity Based Conditional Independence Test and Its Application in Causal Discovery}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/05942-residual-similarity-based-conditional-independence-test-and-its-application-in-causal-discovery/},
}

@inproceedings{neurips2019_triad_59120,
  title = {{Triad Constraints for Learning Causal Structure of Latent Variables}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14235},
}

@inproceedings{neurips2022_latent_65842,
  title = {{Latent Hierarchical Causal Structure Discovery with Rank Constraints}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53783},
}

@inproceedings{neurips2020_generalized_59936,
  title = {{Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18682},
}

@inproceedings{neurips2022_missdag_64027,
  title = {{MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54085},
}

@inproceedings{neurips2022_sound_64961,
  title = {{Sound and Complete Causal Identification with Latent Variables Given Local Background Knowledge}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54784},
}

@inproceedings{aaai2024_identification_6262,
  title = {{Identification of Causal Structure in the Presence of Missing Data with Additive Noise Model}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30036},
}

@inproceedings{neurips2019_selecting_58961,
  title = {{Selecting causal brain features with a single conditional independence test per feature}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14208},
}

@inproceedings{aaai2023_vector_4935,
  title = {{Vector Causal Inference between Two Groups of Variables}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26450},
}

@inproceedings{aaai2024_identification_6263,
  title = {{Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30017},
}

@inproceedings{icml2020_distinguishing_38114,
  title = {{Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6405},
}

@inproceedings{aaai2020_a_103343,
  title = {{A Simultaneous Discover-Identify Approach to Causal Inference in Linear Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6595},
}

@inproceedings{neurips2022_causal_65800,
  title = {{Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54198},
}

@inproceedings{aaai2024_causal_5381,
  title = {{Causal Discovery from Poisson Branching Structural Causal Model Using High-Order Cumulant with Path Analysis}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30037},
}

@inproceedings{aaai2025_local_9807,
  title = {{Local Causal Discovery Without Causal Sufficiency}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34062},
}

@inproceedings{aaai2025_testing_10952,
  title = {{Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34885},
}

@inproceedings{iclr2024_a_34021,
  title = {{A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19054},
}

@inproceedings{icml2024_causal_44003,
  title = {{Causal Discovery with Fewer Conditional Independence Tests}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34449},
}

@inproceedings{icml2024_on_44820,
  title = {{On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33632},
}

@inproceedings{neurips2024_on_72313,
  title = {{On the Identifiability of Poisson Branching Structural Causal Model Using Probability Generating Function}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95033},
}

@inproceedings{neurips2024_causal_74548,
  title = {{Causal Discovery from Event Sequences by Local Cause-Effect Attribution}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93036},
}

@inproceedings{aaai2026_robust_108649,
  title = {{Robust Causal Discovery Under Imperfect Structural Constraints}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/41001},
}

@inproceedings{aaai2025_causal_8395,
  title = {{Causal Discovery by Interventions via Integer Programming}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33810},
}

@inproceedings{icml2024_optimal_43831,
  title = {{Optimal Kernel Choice for Score Function-based Causal Discovery}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34621},
}

@inproceedings{icml2024_causal_45222,
  title = {{Causal Discovery via Conditional Independence Testing with Proxy Variables}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33230},
}

@inproceedings{icml2024_on_45366,
  title = {{On the sample complexity of conditional independence testing with Von Mises estimator with application to causal discovery}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33085},
}

@inproceedings{icml2024_bivariate_45602,
  title = {{Bivariate Causal Discovery using Bayesian Model Selection}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32850},
}

@inproceedings{icml2025_permutation_47519,
  title = {{Permutation-based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45077},
}

@inproceedings{icml2025_latent_47560,
  title = {{Latent Variable Causal Discovery under Selection Bias}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45036},
}

@inproceedings{icml2026_power_55323,
  title = {{Power-Boosted Granger-Causal Discovery for Large Heterogeneous Panel Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=xn0I9CfWvN},
}

@inproceedings{neurips2024_on_73976,
  title = {{On Causal Discovery in the Presence of Deterministic Relations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93539},
}