% Key papers for Latent-variable causal estimation (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2023_gaitgci_21051,
  title = {{GaitGCI: Generative Counterfactual Intervention for Gait Recognition}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/22487},
}

@inproceedings{neurips2020_a_59231,
  title = {{A Causal View on Robustness  of Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18059},
}

@inproceedings{aaai2017_entropic_101423,
  title = {{Entropic Causal Inference}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10674},
}

@inproceedings{aaai2023_spatio_4717,
  title = {{Spatio-Temporal Neural Structural Causal Models for Bike Flow Prediction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25542},
}

@inproceedings{icml2019_neural_37571,
  title = {{Neural Network Attributions: A Causal Perspective}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4003},
}

@inproceedings{cvpr2025_image_26589,
  title = {{Image Quality Assessment: Investigating Causal Perceptual Effects with Abductive Counterfactual Inference}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/32557},
}

@inproceedings{neurips2018_learning_57305,
  title = {{Learning and Testing Causal Models with Interventions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11899},
}

@inproceedings{neurips2018_multi_57402,
  title = {{Multi-domain Causal Structure Learning in Linear Systems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11607},
}

@inproceedings{icml2020_deepmatch_38096,
  title = {{DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6247},
}

@inproceedings{neurips2021_recovering_62870,
  title = {{Recovering Latent Causal Factor for Generalization to Distributional Shifts}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28611},
}

@inproceedings{aaai2020_event_101865,
  title = {{Event-Driven Continuous Time Bayesian Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5725},
}

@inproceedings{iclr2018_implicit_28633,
  title = {{Implicit Causal Models for Genome-wide Association Studies}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/274},
}

@inproceedings{aaai2021_meta_983,
  title = {{Meta Learning for Causal Direction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/09897-meta-learning-for-causal-direction/},
}

@inproceedings{neurips2024_drift_73930,
  title = {{Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93581},
}

@inproceedings{aaai2022_vaca_2930,
  title = {{VACA: Designing Variational Graph Autoencoders for Causal Queries}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/08159-vaca-designing-variational-graph-autoencoders-for-causal-queries/},
}

@inproceedings{neurips2021_miracle_61799,
  title = {{MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27670},
}

@inproceedings{neurips2021_interventional_62483,
  title = {{Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28825},
}

@inproceedings{aaai2023_causal_3241,
  title = {{Causal Conditional Hidden Markov Model for Multimodal Traffic Prediction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25619},
}

@inproceedings{cvpr2024_bi_22838,
  title = {{Bi-Causal: Group Activity Recognition via Bidirectional Causality}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30675},
}

@inproceedings{icml2018_detecting_36632,
  title = {{Detecting non-causal artifacts in multivariate linear regression models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2007},
}

@inproceedings{neurips2024_causal_70218,
  title = {{Causal Deciphering and Inpainting in Spatio-Temporal Dynamics via Diffusion Model}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96868},
}

@inproceedings{aaai2024_towards_7605,
  title = {{Towards Learning and Explaining Indirect Causal Effects in Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29399},
}

@inproceedings{aaai2025_designing_8701,
  title = {{Designing Ambiguity Sets for Distributionally Robust Optimization Using Structural Causal Optimal Transport}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33808},
}

@inproceedings{neurips2025_gene_74773,
  title = {{Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=14irEkV01l},
}

@inproceedings{aaai2025_learning_9750,
  title = {{Learning Structural Causal Models from Ordering: Identifiable Flow Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33961},
}

@inproceedings{iclr2024_exploiting_34234,
  title = {{Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18841},
}

@inproceedings{icml2024_causal_45111,
  title = {{Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33341},
}

@inproceedings{icml2025_e_45900,
  title = {{E-LDA: Toward Interpretable LDA Topic Models with Strong Guarantees in Logarithmic Parallel Time}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46643},
}

@inproceedings{icml2026_causalprofiler_49271,
  title = {{CausalProfiler: Generating Synthetic Benchmarks for Rigorous and Transparent Evaluation of Causal Machine Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=0wCl7EifsY},
}

@inproceedings{icml2026_high_51785,
  title = {{High-Dimensional Sensitivity Analysis for Genomic Studies: An Adversarial Framework for Learning Worst-Case Latent Confounders}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Pt4aUwYJCu},
}

@inproceedings{icml2026_disco_52192,
  title = {{DISCO: Mitigating Bias in Deep Learning with Conditional Distance Correlation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=TYjTWxDOOC},
}

@inproceedings{icml2026_mechanistic_52795,
  title = {{Mechanistic Interpretability as Statistical Estimation: A Variance Analysis}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Z7KqxFpWzN},
}

@inproceedings{icml2026_one_53493,
  title = {{One Intervention per Component is Enough: Towards Identifiability in Linear Stochastic Dynamics from Steady State}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=g07aDcWYJ9},
}

@inproceedings{icml2026_distributionally_54104,
  title = {{Distributionally Robust Causal Abstractions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=lu2d1gQhHS},
}

@inproceedings{neurips2024_partial_70276,
  title = {{Partial Transportability for Domain Generalization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96813},
}

@inproceedings{neurips2024_causal_74498,
  title = {{Causal vs. Anticausal merging of predictors}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93078},
}

@inproceedings{neurips2025_non_76062,
  title = {{Non-Stationary Structural Causal Bandits}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=F4LhOqhxkk},
}