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  author={Zheng, Yujia and Huang, Biwei and Chen, Wei and Ramsey, Joseph and Gong, Mingming and Cai, Ruichu and Shimizu, Shohei and Spirtes, Peter and Zhang, Kun},
  journal={Journal of Machine Learning Research},
  volume={25},
  number={60},
  pages={1--8},
  year={2024}
}

@inproceedings{beinlich1989alarm,
  title={The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks},
  author={Beinlich, Ingo A and Suermondt, Henri Jacques and Chavez, R Martin and Cooper, Gregory F},
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  year={1989},
  organization={Springer}
}

@article{binder1997adaptive,
  title={Adaptive probabilistic networks with hidden variables},
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  pages={213--244},
  year={1997},
  publisher={Springer}
}


@article{hyttinen2013discovering,
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  author={Hyttinen, Antti and Hoyer, Patrik O and Eberhardt, Frederick and Jarvisalo, Matti},
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  year={2013}
}


@inproceedings{hyttinen2014constraint,
  title={Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming.},
  author={Hyttinen, Antti and Eberhardt, Frederick and J{\"a}rvisalo, Matti},
  booktitle={UAI},
  pages={340--349},
  year={2014}
}

@article{zhang2012kernel,
  title={Kernel-based conditional independence test and application in causal discovery},
  author={Zhang, Kun and Peters, Jonas and Janzing, Dominik and Sch{\"o}lkopf, Bernhard},
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  year={2012}
}


@article{strobl2019estimating,
  title={Estimating and controlling the false discovery rate of the pc algorithm using edge-specific p-values},
  author={Strobl, Eric V and Spirtes, Peter L and Visweswaran, Shyam},
  journal={ACM Transactions on Intelligent Systems and Technology (TIST)},
  volume={10},
  number={5},
  pages={1--37},
  year={2019},
  publisher={ACM New York, NY, USA}
}

@article{li2009controlling,
  title={Controlling the false discovery rate of the association/causality structure learned with the PC algorithm.},
  author={Li, Junning and Wang, Z Jane},
  journal={Journal of Machine Learning Research},
  volume={10},
  number={2},
  year={2009}
}



@inproceedings{doran2014permutation,
  title={A Permutation-Based Kernel Conditional Independence Test.},
  author={Doran, Gary and Muandet, Krikamol and Zhang, Kun and Sch{\"o}lkopf, Bernhard},
  booktitle={UAI},
  pages={132--141},
  year={2014}
}

@inproceedings{lee2017self,
  title={Self-discrepancy conditional independence test},
  author={Lee, Sanghack and Honavar, Vasant G},
  booktitle={Uncertainty in artificial intelligence},
  volume={33},
  year={2017}
}

@article{berrett2020conditional,
  title={The conditional permutation test for independence while controlling for confounders},
  author={Berrett, Thomas B and Wang, Yi and Barber, Rina Foygel and Samworth, Richard J},
  journal={Journal of the Royal Statistical Society Series B: Statistical Methodology},
  volume={82},
  number={1},
  pages={175--197},
  year={2020},
  publisher={Oxford University Press}
}

@article{kim2023conditional,
  title={Conditional Independence Testing for Discrete Distributions: Beyond $\chi^2$-and $ G $-tests},
  author={Kim, Ilmun and Neykov, Matey and Balakrishnan, Sivaraman and Wasserman, Larry},
  journal={arXiv preprint arXiv:2308.05373},
  year={2023}
}

@inproceedings{zhang2022residual,
  title={Residual similarity based conditional independence test and its application in causal discovery},
  author={Zhang, Hao and Zhou, Shuigeng and Zhang, Kun and Guan, Jihong},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={36},
  number={5},
  pages={5942--5949},
  year={2022}
}




@article{pensar2016role,
  title={The role of local partial independence in learning of Bayesian networks},
  author={Pensar, Johan and Nyman, Henrik and Lintusaari, Jarno and Corander, Jukka},
  journal={International journal of approximate reasoning},
  volume={69},
  pages={91--105},
  year={2016},
  publisher={Elsevier}
}

@inproceedings{hwang2023discovery,
  title={On discovery of local independence over continuous variables via neural contextual decomposition},
  author={Hwang, Inwoo and Kwak, Yunhyeok and Song, Yeon-Ji and Zhang, Byoung-Tak and Lee, Sanghack},
  booktitle={Conference on Causal Learning and Reasoning},
  pages={448--472},
  year={2023},
  organization={PMLR}
}

@inproceedings{boutilier1996context,
  title={Context-specific independence in Bayesian networks},
  author={Boutilier, Craig and Friedman, Nir and Goldszmidt, Moises and Koller, Daphne},
  booktitle={Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence},
  pages={115--123},
  year={1996}
}

@inproceedings{tsamardinos2010permutation,
  title={Permutation testing improves {B}ayesian network learning},
  author={Tsamardinos, Ioannis and Borboudakis, Giorgos},
  booktitle={Joint European conference on machine learning and knowledge discovery in databases},
  pages={322--337},
  year={2010},
  organization={Springer}
}


@article{JSSv035i03,
	author = {Scutari, Marco},
	journal = {Journal of Statistical Software},
	number = {3},
	pages = {1--22},
	title = {Learning {B}ayesian Networks with the bnlearn {R} Package},
	volume = {35},
	year = {2010},
	bdsk-url-1 = {https://www.jstatsoft.org/index.php/jss/article/view/v035i03},
	bdsk-url-2 = {https://doi.org/10.18637/jss.v035.i03}}


@inproceedings{Abelln2006SomeVO,
  title={Some Variations on the PC Algorithm},
  author={Joaqu{\'i}n Abell{\'a}n and Manuel G{\'o}mez-Olmedo and Seraf{\'i}n Moral},
  booktitle={European Workshop on Probabilistic Graphical Models},
  year={2006},
  url={https://api.semanticscholar.org/CorpusID:13433751}
}

@article{erdHos1960evolution,
  title={On the evolution of random graphs},
  author={Erd{\H{o}}s, Paul and R{\'e}nyi, Alfr{\'e}d and others},
  journal={Publ. math. inst. hung. acad. sci},
  volume={5},
  number={1},
  pages={17--60},
  year={1960}
}

@article{Armen2014EstimationAC,
  title={Estimation and Control of the False Discovery Rate of Bayesian Network Skeleton Identification},
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  year={2014},
journal={Technical Report FORTH-ICS / TR-441 }
}

@article{pena2007towards,
  title={Towards scalable and data efficient learning of {M}arkov boundaries},
  author={Pena, Jose M and Nilsson, Roland and Bj{\"o}rkegren, Johan and Tegn{\'e}r, Jesper},
  journal={International Journal of Approximate Reasoning},
  volume={45},
  number={2},
  pages={211--232},
  year={2007},
  publisher={Elsevier}
}

@article{sachs2005causal,
  title={Causal protein-signaling networks derived from multiparameter single-cell data},
  author={Sachs, Karen and Perez, Omar and Pe'er, Dana and Lauffenburger, Douglas A and Nolan, Garry P},
  journal={Science},
  volume={308},
  number={5721},
  pages={523--529},
  year={2005},
  publisher={American Association for the Advancement of Science}
}

@InProceedings{pmlr-vR4-tsamardinos03a,
  title = 	 {Towards Principled Feature Selection: Relevancy, Filters and Wrappers},
  author =       {Tsamardinos, Ioannis and Aliferis, Constantin F.},
  booktitle = 	 {Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics},
  pages = 	 {300--307},
  year = 	 {2003},
  editor = 	 {Bishop, Christopher M. and Frey, Brendan J.},
  volume = 	 {R4},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {03--06 Jan},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/r4/tsamardinos03a/tsamardinos03a.pdf},
  abstract = 	 {In an influential paper Kohavi and John [7] presented a number of disadvantages of the filter approach to the feature selection problem, steering research towards algorithms adopting the wrapper approach. We show here that neither approach is inherently better and that any practical feature selection algorithm needs to at least consider the learner used for classification and the metric used for evaluating the learner’s performance. In the process we formally define the feature selection problem, re-examine the relationship between relevancy and filter algorithms, and establish a connection between Kohavi and John’s definition of relevancy to the Markov Blanket of a target variable in a Bayesian Network faithful to some data distribution. The theoretical results lead to principled ways of designing optimal filter algorithms of which we present one example.},
  note =         {Reissued by PMLR on 01 April 2021.}
}


@article{JMLR:v11:aliferis10a,
  author  = {Constantin F. Aliferis and Alexander Statnikov and Ioannis Tsamardinos and Subramani Mani and Xenofon D. Koutsoukos},
  title   = {Local Causal and {M}arkov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation},
  journal = {Journal of Machine Learning Research},
  year    = {2010},
  volume  = {11},
  number  = {7},
  pages   = {171--234},
}

@article{JMLR:v11:aliferis10b,
  author  = {Constantin F. Aliferis and Alexander Statnikov and Ioannis Tsamardinos and Subramani Mani and Xenofon D. Koutsoukos},
  title   = {Local Causal and {M}arkov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions},
  journal = {Journal of Machine Learning Research},
  year    = {2010},
  volume  = {11},
  number  = {8},
  pages   = {235--284},
}

@article{tsamardinos:06a,
	author = {Tsamardinos, Ioannis and Brown, Laura E. and Aliferis, Constantin F.},
	date = {2006/10/01},
	id = {Tsamardinos2006},
	isbn = {1573-0565},
	journal = {Machine Learning},
	number = {1},
	pages = {31--78},
	title = {The max-min hill-climbing {B}ayesian network structure learning algorithm},
	volume = {65},
	year = {2006},
}


@article{bromberg09a,
  author  = {Facundo Bromberg and Dimitris Margaritis},
  title   = {Improving the Reliability of Causal Discovery from Small Data Sets Using Argumentation},
  journal = {Journal of Machine Learning Research},
  year    = {2009},
  volume  = {10},
  number  = {12},
  pages   = {301--340},
}

@article{yu2020causality,
  title={Causality-based feature selection: Methods and evaluations},
  author={Yu, Kui and Guo, Xianjie and Liu, Lin and Li, Jiuyong and Wang, Hao and Ling, Zhaolong and Wu, Xindong},
  journal={ACM Computing Surveys (CSUR)},
  volume={53},
  number={5},
  pages={1--36},
  year={2020},
  publisher={ACM New York, NY, USA}
}


@article{leger2022causal,
  title={Causal inference in case of near-violation of positivity: comparison of methods},
  author={L{\'e}ger, Maxime and Chatton, Arthur and Le Borgne, Florent and Pirracchio, Romain and Lasocki, Sigismond and Foucher, Yohann},
  journal={Biometrical Journal},
  volume={64},
  number={8},
  pages={1389--1403},
  year={2022},
  publisher={Wiley Online Library}
}

@article{petersen2012diagnosing,
  title={Diagnosing and responding to violations in the positivity assumption},
  author={Petersen, Maya L and Porter, Kristin E and Gruber, Susan and Wang, Yue and Van Der Laan, Mark J},
  journal={Statistical methods in medical research},
  volume={21},
  number={1},
  pages={31--54},
  year={2012},
  publisher={Sage Publications Sage UK: London, England}
}

@article{dahabreh2019generalizing,
  title={Generalizing causal inferences from randomized trials: counterfactual and graphical identification},
  author={Dahabreh, Issa J and Robins, James M and Haneuse, Sebastien JP and Hern{\'a}n, Miguel A},
  journal={arXiv preprint arXiv:1906.10792},
  year={2019}
}


@article{zhang2021non,
  title={Non-parametric methods for partial identification of causal effects},
  author={Zhang, Junzhe and Bareinboim, Elias},
  journal={Columbia CausalAI Laboratory Technical Report},
  year={2021}
}

@inproceedings{zhang2022partial,
  title={Partial counterfactual identification from observational and experimental data},
  author={Zhang, Junzhe and Tian, Jin and Bareinboim, Elias},
  booktitle={International Conference on Machine Learning},
  pages={26548--26558},
  year={2022},
  organization={PMLR}
}

@inproceedings{li2022bounds,
  title={Bounds on causal effects and application to high dimensional data},
  author={Li, Ang and Pearl, Judea},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  pages={5773--5780},
  year={2022}
}

@book{pearl2009causality,
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  author={Pearl, Judea},
  year={2009},
  publisher={Cambridge university press}
}

@incollection{pearl2022probabilities,
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  author={Pearl, Judea},
  booktitle={Probabilistic and Causal Inference: The Works of Judea Pearl},
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  year={2022}
}

@article{tian2000probabilities,
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@book{hernan2020,
    title = {{Causal Inference: What if.}},
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@article{tikka2017simplifying,
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  pages={1203--1232},
  year={2017},
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} %% projection 




@inproceedings{jung2020estimating,
  title={Estimating causal effects using weighting-based estimators},
  author={Jung, Yonghan and Tian, Jin and Bareinboim, Elias},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={06},
  pages={10186--10193},
  year={2020}
}  %% mentioned - Napkin 


@incollection{verma1990causal,
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@article{hernan2006estimating,
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} %% mentioned 

@book{baron2019probability,
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  year={2019},
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} %% read but not mentioned in text

@article{kolmogorov1933grundbegriffe,
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} %% mentioned in text

@book{gut2005probability,
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  volume={200},
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} %% used -  first introduced "backdoor criterion" ... 

@article{pearl2012calculus,
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  author={Pearl, Judea},
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  year={2012}
} %% used 

@article{li2023epsilon,
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  author={Li, Ang and Mueller, Scott and Pearl, Judea},
  journal={arXiv preprint arXiv:2301.12022},
  year={2023}
}


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    year = {2001},
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@inproceedings{correa:bar20-sdo,
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    year = {2020},
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    author = {Correa, Juan D and Bareinboim, Elias},
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}

@article{good:83,
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    year = {1983},
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@article{good:61,
    title = {{A Causal Calculus {\{}(I){\}}}},
    year = {1961},
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@article{good:61b,
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    year = {1961},
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}

@article{good:62,
    title = {{A Causal Calculus {\{}(II){\}}}},
    year = {1962},
    journal = {British Journal for the Philosophy of Science},
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@article{good:62b,
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    year = {1962},
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@misc{good:61-62,
    title = {{A Causal Calculus, {\{}I-II{\}}, {\{}{\{}{\textbackslash}em British Journal for the Philosophy of Science{\}}{\}}, 11:305--318}},
    year = {1961},
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@article{andersson:etal97b,
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@article{chickering:pea97,
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@inproceedings{dekleer:89,
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@inproceedings{perkovic:etal15,
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    editor = {{M. Meila and T. Heskes (Eds.)}},
    pages = {682--691},
    publisher = {AUAI Press},
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}

@article{shpitser:van11,
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@phdthesis{lee:67,
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@article{ledesma:etal97,
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@inproceedings{kim:pea83,
    title = {{A computational model for combined causal and diagnostic reasoning in inference systems}},
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}

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@inproceedings{pearl:85a,
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@techreport{Correa2016,
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@incollection{halpern:pea01b,
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@inproceedings{halpern:pea01a,
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