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

@inproceedings{icml2018_learning_36789,
  title = {{Learning Independent Causal Mechanisms}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2049},
}

@inproceedings{neurips2018_domain_57067,
  title = {{Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/12025},
}

@inproceedings{aaai2021_estimating_539,
  title = {{Estimating Identifiable Causal Effects through Double Machine Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/12113-estimating-identifiable-causal-effects-through-double-machine-learning/},
}

@inproceedings{neurips2018_removing_57567,
  title = {{Removing Hidden Confounding by Experimental Grounding}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/12029},
}

@inproceedings{icml2019_sensitivity_37734,
  title = {{Sensitivity Analysis of Linear Structural Causal Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4243},
}

@inproceedings{neurips2018_identification_57218,
  title = {{Identification and Estimation of Causal Effects from Dependent Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11897},
}

@inproceedings{icml2019_causal_37211,
  title = {{Causal Identification under Markov Equivalence: Completeness Results}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3841},
}

@inproceedings{aaai2019_building_100194,
  title = {{Building Causal Graphs from Medical Literature and Electronic Medical Records}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/3902},
}

@inproceedings{aaai2019_identification_101325,
  title = {{Identification of Causal Effects in the Presence of Selection Bias}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4125},
}

@inproceedings{neurips2020_how_60020,
  title = {{How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/19064},
}

@inproceedings{neurips2019_the_59057,
  title = {{The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14149},
}

@inproceedings{neurips2020_learning_60154,
  title = {{Learning Causal Effects via Weighted Empirical Risk Minimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/19052},
}

@inproceedings{aaai2020_general_103330,
  title = {{General Transportability – Synthesizing Observations and Experiments from Heterogeneous Domains}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6582},
}

@inproceedings{neurips2018_causal_56935,
  title = {{Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11509},
}

@inproceedings{aaai2021_equivalent_534,
  title = {{Equivalent Causal Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/06202-equivalent-causal-models/},
}

@inproceedings{icml2017_identification_36223,
  title = {{Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/783},
}

@inproceedings{neurips2019_identifying_58387,
  title = {{Identifying Causal Effects via Context-specific Independence Relations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13466},
}

@inproceedings{neurips2023_identifiability_69431,
  title = {{Identifiability Guarantees for Causal Disentanglement from Soft Interventions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/70478},
}

@inproceedings{aaai2022_identification_2221,
  title = {{Identification of Linear Latent Variable Model with Arbitrary Distribution}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/06350-identification-of-linear-latent-variable-model-with-arbitrary-distribution/},
}

@inproceedings{icml2020_causal_38002,
  title = {{Causal Effect Identifiability under Partial-Observability}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6440},
}

@inproceedings{aaai2025_a_7918,
  title = {{A Practical Approach to Causal Inference over Time}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33626},
}

@inproceedings{aaai2024_identifiability_6260,
  title = {{Identifiability of Direct Effects from Summary Causal Graphs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30021},
}

@inproceedings{neurips2024_learning_70967,
  title = {{Learning Identifiable Factorized Causal Representations of Cellular Responses}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96237},
}

@inproceedings{neurips2024_identifying_71925,
  title = {{Identifying Causal Effects Under Functional Dependencies}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95380},
}

@inproceedings{neurips2024_detecting_72277,
  title = {{Detecting and Measuring Confounding Using Causal Mechanism Shifts}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95068},
}

@inproceedings{neurips2024_causal_73962,
  title = {{Causal Dependence Plots}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93550},
}

@inproceedings{aaai2025_causal_8396,
  title = {{Causal Explanation of Quality of Parent-Child Interactions with Multimodal Behavioral Features (Student Abstract)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35258},
}

@inproceedings{iclr2024_causal_35572,
  title = {{Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17501},
}

@inproceedings{icml2024_causal_43750,
  title = {{Causal Effect Identification in LiNGAM Models with Latent Confounders}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34702},
}

@inproceedings{neurips2024_do_70477,
  title = {{Do Finetti: On Causal Effects for Exchangeable Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96631},
}

@inproceedings{neurips2024_complete_70613,
  title = {{Complete Graphical Criterion for Sequential Covariate Adjustment in Causal Inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96506},
}

@inproceedings{neurips2024_conditional_74386,
  title = {{Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93180},
}

@inproceedings{neurips2025_root_75376,
  title = {{Root Cause Analysis of Outliers with Missing Structural Knowledge}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=7Nxq4RQApu},
}

@inproceedings{aaai2024_to_7568,
  title = {{To Know the Causes of Things: Text Mining for Causal Relations}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30413},
}

@inproceedings{aaai2024_s_7855,
  title = {{s-ID: Causal Effect Identification in a Sub-population}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30011},
}

@inproceedings{aaai2025_identifying_9500,
  title = {{Identifying Macro Conditional Independencies and Macro Total Effects in Summary Causal Graphs with Latent Confounding}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34882},
}

@inproceedings{iclr2024_causal_33880,
  title = {{Causal-StoNet: Causal Inference for High-Dimensional Complex Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19195},
}

@inproceedings{icml2024_multiply_45308,
  title = {{Multiply-Robust Causal Change Attribution}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33144},
}

@inproceedings{icml2025_generative_46697,
  title = {{Generative Intervention Models for Causal Perturbation Modeling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45875},
}

@inproceedings{icml2025_position_47107,
  title = {{Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/40145},
}