% Key papers for Causal representation identifiability (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{aaai2020_explainable_101234,
  title = {{Explainable Reinforcement Learning through a Causal Lens}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5631},
}

@inproceedings{cvpr2021_causalvae_16697,
  title = {{CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{icml2017_schema_36385,
  title = {{Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/652},
}

@inproceedings{neurips2019_causal_57982,
  title = {{Causal Confusion in Imitation Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13241},
}

@inproceedings{cvpr2020_two_16387,
  title = {{Two Causal Principles for Improving Visual Dialog}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{iclr2020_a_29911,
  title = {{A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=ryxWIgBFPS},
}

@inproceedings{neurips2021_self_61328,
  title = {{Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28098},
}

@inproceedings{cvpr2021_generative_17075,
  title = {{Generative Interventions for Causal Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2023_layout_21329,
  title = {{Layout-Based Causal Inference for Object Navigation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/22762},
}

@inproceedings{aaai2022_causal_1826,
  title = {{Causal Intervention for Subject-Deconfounded Facial Action Unit Recognition}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/00374-causal-intervention-for-subject-deconfounded-facial-action-unit-recognition/},
}

@inproceedings{aaai2023_causal_3244,
  title = {{Causal Intervention for Human Trajectory Prediction with Cross Attention Mechanism}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25142},
}

@inproceedings{aaai2022_interventional_2262,
  title = {{Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/01601-interventional-multi-instance-learning-with-deconfounded-instance-level-prediction/},
}

@inproceedings{aaai2022_learning_2320,
  title = {{Learning Human Driving Behaviors with Sequential Causal Imitation Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/04583-learning-human-driving-behaviors-with-sequential-causal-imitation-learning/},
}

@inproceedings{icml2019_robustly_37707,
  title = {{Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4055},
}

@inproceedings{aaai2022_on_2487,
  title = {{On Causally Disentangled Representations}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/08089-on-causally-disentangled-representations/},
}

@inproceedings{neurips2020_generative_59943,
  title = {{Generative causal explanations of black-box classifiers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18919},
}

@inproceedings{neurips2022_weakly_65536,
  title = {{Weakly supervised causal representation learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54819},
}

@inproceedings{neurips2019_a_57828,
  title = {{A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13545},
}

@inproceedings{neurips2023_unpaired_70086,
  title = {{Unpaired Multi-Domain Causal Representation Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/69890},
}

@inproceedings{neurips2022_temporally_65125,
  title = {{Temporally Disentangled Representation Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54358},
}

@inproceedings{aaai2024_causal_5382,
  title = {{Causal Representation Learning via Counterfactual Intervention}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28108},
}

@inproceedings{aaai2024_task_7503,
  title = {{Task-Driven Causal Feature Distillation: Towards Trustworthy Risk Prediction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29047},
}

@inproceedings{iclr2024_multi_34326,
  title = {{Multi-View Causal Representation Learning with Partial Observability}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18749},
}

@inproceedings{aaai2024_mastering_6607,
  title = {{Mastering Context-to-Label Representation Transformation for Event Causality Identification with Diffusion Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29840},
}

@inproceedings{neurips2024_from_74071,
  title = {{From Causal to Concept-Based Representation Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93459},
}

@inproceedings{aaai2024_neural_6761,
  title = {{Neural Causal Abstractions}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30044},
}

@inproceedings{neurips2024_marrying_71776,
  title = {{Marrying Causal Representation Learning with Dynamical Systems for Science}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95516},
}

@inproceedings{neurips2024_learning_73068,
  title = {{Learning Linear Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94358},
}

@inproceedings{iclr2024_identifying_33573,
  title = {{Identifying Representations for Intervention Extrapolation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19502},
}

@inproceedings{icml2024_predictive_45331,
  title = {{Predictive Coding beyond Correlations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33121},
}

@inproceedings{icml2025_teaching_46406,
  title = {{Teaching Transformers Causal Reasoning through Axiomatic Training}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46158},
}

@inproceedings{neurips2024_sample_72602,
  title = {{Sample Complexity of Interventional Causal Representation Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94775},
}

@inproceedings{neurips2024_linear_74435,
  title = {{Linear Causal Representation Learning from Unknown Multi-node Interventions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93136},
}

@inproceedings{iclr2024_identifiable_35014,
  title = {{Identifiable Latent Polynomial Causal Models through the Lens of Change}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18060},
}

@inproceedings{iclr2024_object_35370,
  title = {{Object centric architectures enable efficient causal representation learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17703},
}

@inproceedings{icml2024_causal_44347,
  title = {{Causal Representation Learning from Multiple Distributions: A General Setting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34105},
}

@inproceedings{icml2024_modular_44809,
  title = {{Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33643},
}

@inproceedings{icml2025_mib_48786,
  title = {{MIB: A Mechanistic Interpretability Benchmark}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43836},
}

@inproceedings{neurips2024_towards_70304,
  title = {{Towards Understanding Extrapolation: a Causal Lens}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96789},
}

@inproceedings{neurips2024_learning_72924,
  title = {{Learning Discrete Concepts in Latent Hierarchical Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94487},
}