causal.bib

@inproceedings{abdulaal2024GraphDiscovery,
  title={Causal Modelling Agents: Causal Graph Discovery through Synergising Metadata- and Data-driven Reasoning},
  author={Ahmed Abdulaal and adamos hadjivasiliou and Nina Montana-Brown and Tiantian He and Ayodeji Ijishakin and Ivana Drobnjak and Daniel C. Castro and Daniel C. Alexander},
  booktitle={The Twelfth International Conference on Learning Representations},
  year={2024},
  url={https://openreview.net/pdf?id=pAoqRlTBtY},
  note={Articulo inicial}
}

@misc{kıcıman2024causalreasoninglargelanguage,
  title={Causal Reasoning and Large Language Models: Opening a New Frontier for Causality},
  author={Emre Kıcıman and Robert Ness and Amit Sharma and Chenhao Tan},
  year={2024},
  eprint={2305.00050},
  archivePrefix={arXiv},
  primaryClass={cs.AI},
  url={https://arxiv.org/pdf/2305.00050},
  note = {``Large Language Models (LLMs) establish new state-of-the-art performance on multiple causal benchmarks including counterfactual reasoning, actual causality, and causal discovery''~\cite{abdulaal2024GraphDiscovery}}
}

@inproceedings{pawlowski2020DSCM,
 author = {Pawlowski, Nick and Coelho de Castro, Daniel and Glocker, Ben},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
 pages = {857--869},
 publisher = {Curran Associates, Inc.},
 title = {Deep Structural Causal Models for Tractable Counterfactual Inference},
 url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/0987b8b338d6c90bbedd8631bc499221-Paper.pdf},
 volume = {33},
 year = {2020},
 note = {Seminal paper in Deep Structural Causal Model cited by abdulaal2024GraphDiscovery}
}


@inproceedings{sheth2024causalgraphllm,
  title={CausalGraph2{LLM}: Evaluating {LLM}s for Causal Queries},
  author={Ivaxi Sheth and Bahare Fatemi and Mario Fritz},
  booktitle={Causality and Large Models @NeurIPS 2024},
  year={2024},
  url={https://openreview.net/pdf?id=wqir4sG2Bc},
  note = {Encontrado en }
}

@InProceedings{khemakhem2021CausalAutoregressiveFlows,
  title = 	 { Causal Autoregressive Flows },
  author =       {Khemakhem, Ilyes and Monti, Ricardo and Leech, Robert and Hyvarinen, Aapo},
  booktitle = 	 {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics},
  pages = 	 {3520--3528},
  year = 	 {2021},
  editor = 	 {Banerjee, Arindam and Fukumizu, Kenji},
  volume = 	 {130},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {13--15 Apr},
  publisher =    {PMLR},
  url = {http://proceedings.mlr.press/v130/khemakhem21a/khemakhem21a.pdf},
  note = {A Deep Structural Causal Model cited by abdulaal2024GraphDiscovery}<
}

@inproceedings{sanchez2022diffusion,
  title={Diffusion Causal Models for Counterfactual Estimation},
  author={Pedro Sanchez and Sotirios A. Tsaftaris},
  booktitle={First Conference on Causal Learning and Reasoning},
  year={2022},
  url={https://openreview.net/pdf?id=LAAZLZIMN-o},
  note = {A Deep Structural Causal Model cited by abdulaal2024GraphDiscovery}
}