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

@inproceedings{neurips2020_high_60013,
  title = {{High-recall causal discovery for autocorrelated time series with latent confounders}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18623},
}

@inproceedings{aaai2024_cuts_5364,
  title = {{CUTS+: High-Dimensional Causal Discovery from Irregular Time-Series}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29034},
}

@inproceedings{cvpr2021_causal_16696,
  title = {{Causal Hidden Markov Model for Time Series Disease Forecasting}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{aaai2024_root_7190,
  title = {{Root Cause Analysis in Microservice Using Neural Granger Causal Discovery}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/27772},
}

@inproceedings{iclr2021_interpretable_30112,
  title = {{Interpretable Models for Granger Causality Using Self-explaining Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2605},
}

@inproceedings{icml2019_causal_37210,
  title = {{Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4045},
}

@inproceedings{icml2020_time_38864,
  title = {{Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6131},
}

@inproceedings{icml2021_necessary_39652,
  title = {{Necessary and sufficient conditions for causal feature selection in time series with latent common causes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8441},
}

@inproceedings{iclr2023_cuts_32657,
  title = {{CUTS: Neural Causal Discovery from Irregular Time-Series Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11272},
}

@inproceedings{aaai2024_towards_7630,
  title = {{Towards the Disappearing Truth: Fine-Grained Joint Causal Influences Learning with Hidden Variable-Driven Causal Hypergraphs in Time Series}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29662},
}

@inproceedings{iclr2020_economy_29735,
  title = {{Economy Statistical Recurrent Units For Inferring Nonlinear Granger Causality}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=SyxV9ANFDH},
}

@inproceedings{icml2017_uncertainty_36436,
  title = {{Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/479},
}

@inproceedings{neurips2020_causal_59520,
  title = {{Causal analysis of Covid-19 Spread in Germany}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17722},
}

@inproceedings{aaai2025_spacetime_10626,
  title = {{SPACETIME: Causal Discovery from Non-Stationary Time Series}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34136},
}

@inproceedings{iclr2021_latent_29997,
  title = {{Latent Convergent Cross Mapping}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2939},
}

@inproceedings{aaai2019_estimating_101587,
  title = {{Estimating the Causal Effect from Partially Observed Time Series}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4281},
}

@inproceedings{neurips2017_discovering_56258,
  title = {{Discovering Potential Correlations via Hypercontractivity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9235},
}

@inproceedings{iclr2024_causaltime_35015,
  title = {{CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18059},
}

@inproceedings{icml2025_discovering_47577,
  title = {{Discovering Latent Causal Graphs from Spatiotemporal Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45019},
}

@inproceedings{neurips2023_causal_68840,
  title = {{Causal Discovery in Semi-Stationary Time Series}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71016},
}

@inproceedings{neurips2025_uncle_80111,
  title = {{UnCLe: Towards Scalable Dynamic Causal Discovery in Non-linear Temporal Systems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=wGZPNMEHjP},
}

@inproceedings{neurips2025_exploring_80266,
  title = {{Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal Dependencies in Complex Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=xtHJ0eNEUv},
}

@inproceedings{iclr2024_a_35156,
  title = {{A Dynamical View of the Question of Why}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17917},
}

@inproceedings{icml2024_discovering_44854,
  title = {{Discovering Mixtures of Structural Causal Models from Time Series Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33598},
}

@inproceedings{icml2025_arrow_46482,
  title = {{Arrow: Accelerator for Time Series Causal Discovery with Time Weaving}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46084},
}

@inproceedings{icml2025_generating_47587,
  title = {{Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45009},
}

@inproceedings{icml2026_timesae_49950,
  title = {{TimeSAE: Causal Sparse Decoding for Faithful Explanations of Black-Box Time Series Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=7X5GcRLHhx},
}

@inproceedings{icml2026_robust_49970,
  title = {{Robust Causal Discovery in Real-World Time Series with Power-Laws}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=7i8d203tky},
}

@inproceedings{icml2026_function_51349,
  title = {{Function-Valued Causal Influence in Nonlinear Time Series}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=LF5rPrxbHp},
}

@inproceedings{icml2026_learning_51466,
  title = {{Learning General Causal Structures with Hidden Dynamic Process for Climate Analysis}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=MZxhwFT7yF},
}

@inproceedings{iclr2026_tcd_86181,
  title = {{TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=MtdrOCLAGY},
}

@inproceedings{iclr2026_coarse_88609,
  title = {{Coarse-to-Fine Learning of Dynamic Causal Structures}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=ooqnLFagKq},
}

@inproceedings{icml2024_learning_43499,
  title = {{Learning Causal Relations from Subsampled Time Series with Two Time-Slices}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34953},
}

@inproceedings{icml2025_causal_48297,
  title = {{Causal Discovery from Conditionally Stationary Time Series}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44317},
}