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# Journal papers and conference proceedings
@article{Buhlmann2013,
  author  = {Bühlmann, Peter and Peters, Jonas and Ernest, Jan},
  year    = {2013},
  month   = {10},
  pages   = {},
  title   = {CAM: Causal Additive Models, high-dimensional order search and penalized regression},
  volume  = {42},
  journal = {The Annals of Statistics},
  doi     = {10.1214/14-AOS1260}
}

@article{Colombo2012,
  author    = {Diego Colombo and Marloes H. Maathuis and Markus Kalisch and Thomas S. Richardson},
  title     = {{Learning high-dimensional directed acyclic graphs with latent and selection variables}},
  volume    = {40},
  journal   = {The Annals of Statistics},
  number    = {1},
  publisher = {Institute of Mathematical Statistics},
  pages     = {294 -- 321},
  keywords  = {Causal structure learning, consistency, FCI algorithm, high-dimensionality, maximal ancestral graphs (MAGs), partial ancestral graphs (PAGs), RFCI algorithm, Sparsity},
  year      = {2012},
  doi       = {10.1214/11-AOS940},
  url       = {https://doi.org/10.1214/11-AOS940}
}


@inproceedings{correa2020calculus,
  title     = {A calculus for stochastic interventions: Causal effect identification and surrogate experiments},
  author    = {Correa, Juan and Bareinboim, Elias},
  booktitle = {Proceedings of the AAAI conference on artificial intelligence},
  volume    = {34},
  number    = {06},
  pages     = {10093--10100},
  year      = {2020}
}

@article{Jaber2020causal,
  title   = {Causal discovery from soft interventions with unknown targets: Characterization and learning},
  author  = {Jaber, Amin and Kocaoglu, Murat and Shanmugam, Karthikeyan and Bareinboim, Elias},
  journal = {Advances in neural information processing systems},
  volume  = {33},
  pages   = {9551--9561},
  year    = {2020}
}

@article{Kocaoglu2019characterization,
  title   = {Characterization and learning of causal graphs with latent variables from soft interventions},
  author  = {Kocaoglu, Murat and Jaber, Amin and Shanmugam, Karthikeyan and Bareinboim, Elias},
  journal = {Advances in Neural Information Processing Systems},
  volume  = {32},
  year    = {2019}
}

@article{Li2017,
  author  = {Li, Yingzhen and Turner, Richard},
  journal = {International Conference on Learning Representations (ICLR)},
  year    = {2017},
  month   = {05},
  pages   = {},
  title   = {Gradient Estimators for Implicit Models}
}

@article{Meek1995,
  author  = {Meek, Christopher},
  year    = {2013},
  month   = {02},
  pages   = {},
  title   = {Causal Inference and Causal Explanation with Background Knowledge},
  volume  = {2},
  journal = {Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, QU}
}

@inproceedings{Montagna2023a,
title={Scalable Causal Discovery with Score Matching},
author={Francesco Montagna and Nicoletta Noceti and Lorenzo Rosasco and Kun Zhang and Francesco Locatello},
booktitle={2nd Conference on Causal Learning and Reasoning},
year={2023},
url={https://openreview.net/forum?id=6VvoDjLBPQV}
}

@inproceedings{Montagna2023b,
title={Causal Discovery with Score Matching on Additive Models with Arbitrary Noise},
author={Francesco Montagna and Nicoletta Noceti and Lorenzo Rosasco and Kun Zhang and Francesco Locatello},
booktitle={2nd Conference on Causal Learning and Reasoning},
year={2023},
url={https://openreview.net/forum?id=rVO0Bx90deu}
}

@book{Neapolitan2003,
  author    = {Neapolitan, Richard},
  year      = {2003},
  month     = {01},
  pages     = {},
  title     = {Learning Bayesian Networks},
  isbn      = {9780123704771},
  publisher = {Pearson},
  doi       = {10.1145/1327942.1327961}
}

@inproceedings{rolland2022,
  title     = {Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models},
  author    = {Rolland, Paul and Cevher, Volkan and Kleindessner, Matth{\"a}us and Russell, Chris and Janzing, Dominik and Sch{\"o}lkopf, Bernhard and Locatello, Francesco},
  booktitle = {Proceedings of the 39th International Conference on Machine Learning},
  pages     = {18741--18753},
  year      = {2022},
  editor    = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
  volume    = {162},
  series    = {Proceedings of Machine Learning Research},
  month     = {17--23 Jul},
  publisher = {PMLR}
}

@article{uhler2013geometry,
  title     = {Geometry of the faithfulness assumption in causal inference},
  author    = {Uhler, Caroline and Raskutti, Garvesh and B{\"u}hlmann, Peter and Yu, Bin},
  journal   = {The Annals of Statistics},
  pages     = {436--463},
  year      = {2013},
  publisher = {JSTOR}
}

@article{Zhang2008,
  title    = {On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias},
  journal  = {Artificial Intelligence},
  volume   = {172},
  number   = {16},
  pages    = {1873-1896},
  year     = {2008},
  issn     = {0004-3702},
  doi      = {https://doi.org/10.1016/j.artint.2008.08.001},
  url      = {https://www.sciencedirect.com/science/article/pii/S0004370208001008},
  author   = {Jiji Zhang},
  keywords = {Ancestral graphs, Automated causal discovery, Bayesian networks, Causal models, Markov equivalence, Latent variables}
}

@article{zhang2008ancestralgraphs,
  author  = {Jiji Zhang},
  title   = {Causal Reasoning with Ancestral Graphs},
  journal = {Journal of Machine Learning Research},
  year    = {2008},
  volume  = {9},
  number  = {47},
  pages   = {1437--1474},
  url     = {http://jmlr.org/papers/v9/zhang08a.html}
}

# Books

@book{Pearl_causality_2009,
  author    = {Pearl, Judea},
  title     = {Causality: Models, Reasoning and Inference},
  year      = {2009},
  isbn      = {052189560X},
  publisher = {Cambridge University Press},
  address   = {USA},
  edition   = {2nd}
}

@book{Spirtes1993,
  author    = {Spirtes, Peter and Glymour, Clark and Scheines, Richard},
  year      = {1993},
  month     = {01},
  pages     = {},
  title     = {Causation, Prediction, and Search},
  volume    = {81},
  isbn      = {978-1-4612-7650-0},
  doi       = {10.1007/978-1-4612-2748-9},
  publisher = {The MIT Press}
}

@article{xie2020generalized,
  title   = {Generalized independent noise condition for estimating latent variable causal graphs},
  author  = {Xie, Feng and Cai, Ruichu and Huang, Biwei and Glymour, Clark and Hao, Zhifeng and Zhang, Kun},
  journal = {Advances in Neural Information Processing Systems},
  volume  = {33},
  pages   = {14891--14902},
  year    = {2020}
}

@article{dai2022independence,
  title   = {Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models},
  author  = {Dai, Haoyue and Spirtes, Peter and Zhang, Kun},
  journal = {arXiv preprint arXiv:2210.11021},
  year    = {2022}
}

% Conditional Testing

@article{cressieread1984,
  issn      = {00359246},
  url       = {http://www.jstor.org/stable/2345686},
  author    = {Noel Cressie and Timothy R. C. Read},
  journal   = {Journal of the Royal Statistical Society. Series B (Methodological)},
  number    = {3},
  pages     = {440--464},
  publisher = {[Royal Statistical Society, Wiley]},
  title     = {Multinomial Goodness-of-Fit Tests},
  urldate   = {2023-03-16},
  volume    = {46},
  year      = {1984}
}



@article{frenzel_partial_2007,
  title   = {Partial {Mutual} {Information} for {Coupling} {Analysis} of {Multivariate} {Time} {Series}},
  volume  = {99},
  doi     = {10.1103/PhysRevLett.99.204101},
  journal = {Physical review letters},
  author  = {Frenzel, Stefan and Pompe, Bernd},
  month   = dec,
  year    = {2007},
  pages   = {204101},
  file    = {Full Text PDF:/Users/adam2392/Zotero/storage/8ICFXVZG/Frenzel and Pompe - 2007 - Partial Mutual Information for Coupling Analysis o.pdf:application/pdf}
}

@article{kraskov_estimating_2004,
  title   = {Estimating mutual information},
  volume  = {69},
  url     = {https://link.aps.org/doi/10.1103/PhysRevE.69.066138},
  doi     = {10.1103/PhysRevE.69.066138},
  number  = {6},
  urldate = {2023-01-27},
  journal = {Physical Review E},
  author  = {Kraskov, Alexander and Stögbauer, Harald and Grassberger, Peter},
  month   = jun,
  year    = {2004},
  note    = {Publisher: American Physical Society},
  pages   = {066138}
}

@article{Lopez2016revisiting,
  title   = {Revisiting classifier two-sample tests},
  author  = {Lopez-Paz, David and Oquab, Maxime},
  journal = {arXiv preprint arXiv:1610.06545},
  year    = {2016}
}

@inproceedings{Mukherjee2020ccmi,
  title        = {CCMI: Classifier based conditional mutual information estimation},
  author       = {Mukherjee, Sudipto and Asnani, Himanshu and Kannan, Sreeram},
  booktitle    = {Uncertainty in artificial intelligence},
  pages        = {1083--1093},
  year         = {2020},
  organization = {PMLR}
}

@inproceedings{Park2021conditional,
  title        = {Conditional distributional treatment effect with kernel conditional mean embeddings and U-statistic regression},
  author       = {Park, Junhyung and Shalit, Uri and Sch{\"o}lkopf, Bernhard and Muandet, Krikamol},
  booktitle    = {International Conference on Machine Learning},
  pages        = {8401--8412},
  year         = {2021},
  organization = {PMLR}
}

@inproceedings{Runge2018cmi,
  title     = {Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information},
  author    = {Runge, Jakob},
  booktitle = {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics},
  pages     = {938--947},
  year      = {2018},
  editor    = {Storkey, Amos and Perez-Cruz, Fernando},
  volume    = {84},
  series    = {Proceedings of Machine Learning Research},
  month     = {09--11 Apr},
  publisher = {PMLR}
}

@article{Sen2017model,
  title   = {Model-powered conditional independence test},
  author  = {Sen, Rajat and Suresh, Ananda Theertha and Shanmugam, Karthikeyan and Dimakis, Alexandros G and Shakkottai, Sanjay},
  journal = {Advances in neural information processing systems},
  volume  = {30},
  year    = {2017}
}


@inproceedings{Yu2020Bregman,
  title     = {Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications},
  author    = {Yu, Shujian and Shaker, Ammar and Alesiani, Francesco and Principe, Jose},
  booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
               Artificial Intelligence, {IJCAI-20}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Christian Bessiere},
  pages     = {2777--2784},
  year      = {2020},
  month     = {7},
  note      = {Main track},
  doi       = {10.24963/ijcai.2020/385},
  url       = {https://doi.org/10.24963/ijcai.2020/385}
}

@inproceedings{Zhang2011,
  author    = {Zhang, Kun and Peters, Jonas and Janzing, Dominik and Sch\"{o}lkopf, Bernhard},
  title     = {Kernel-Based Conditional Independence Test and Application in Causal Discovery},
  year      = {2011},
  isbn      = {9780974903972},
  publisher = {AUAI Press},
  address   = {Arlington, Virginia, USA},
  booktitle = {Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence},
  pages     = {804–813},
  numpages  = {10},
  location  = {Barcelona, Spain},
  series    = {UAI'11}
}

% Example refs

@article{sachsdataset2005,
  author  = {Karen Sachs  and Omar Perez  and Dana Pe'er  and Douglas A. Lauffenburger  and Garry P. Nolan },
  title   = {Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data},
  journal = {Science},
  volume  = {308},
  number  = {5721},
  pages   = {523-529},
  year    = {2005},
  doi     = {10.1126/science.1105809},
  url     = {https://www.science.org/doi/abs/10.1126/science.1105809},
  eprint  = {https://www.science.org/doi/pdf/10.1126/science.1105809}
}

