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In ACM Conference on Computer and Communications Security (CCS)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3460120.3484749"},{"key":"e_1_3_2_1_31_1","volume-title":"Mario Fritz, and Yang Zhang.","author":"Liu Yugeng","year":"2021","unstructured":"Yugeng Liu , Rui Wen , Xinlei He , Ahmed Salem , Zhikun Zhang , Michael Backes , Emiliano De Cristofaro , Mario Fritz, and Yang Zhang. 2021 b. ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models . arXiv preprint arXiv:2102.02551 (2021). Yugeng Liu, Rui Wen, Xinlei He, Ahmed Salem, Zhikun Zhang, Michael Backes, Emiliano De Cristofaro, Mario Fritz, and Yang Zhang. 2021b. ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models. arXiv preprint arXiv:2102.02551 (2021)."},{"key":"e_1_3_2_1_32_1","volume-title":"Understanding membership inferences on well-generalized learning models. arXiv preprint arXiv:1802.04889","author":"Long Yunhui","year":"2018","unstructured":"Yunhui Long , Vincent Bindschaedler , Lei Wang , Diyue Bu , Xiaofeng Wang , Haixu Tang , Carl A Gunter , and Kai Chen . 2018. Understanding membership inferences on well-generalized learning models. arXiv preprint arXiv:1802.04889 ( 2018 ). Yunhui Long, Vincent Bindschaedler, Lei Wang, Diyue Bu, Xiaofeng Wang, Haixu Tang, Carl A Gunter, and Kai Chen. 2018. Understanding membership inferences on well-generalized learning models. arXiv preprint arXiv:1802.04889 (2018)."},{"key":"e_1_3_2_1_33_1","volume-title":"Causality in the social sciences. 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Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks. arXiv preprint arXiv:2203.03929 (2022)."},{"key":"e_1_3_2_1_35_1","volume-title":"ML Privacy Meter: Aiding regulatory compliance by quantifying the privacy risks of machine learning. arXiv preprint arXiv:2007.09339","author":"Murakonda Sasi Kumar","year":"2020","unstructured":"Sasi Kumar Murakonda and Reza Shokri . 2020. ML Privacy Meter: Aiding regulatory compliance by quantifying the privacy risks of machine learning. arXiv preprint arXiv:2007.09339 ( 2020 ). Sasi Kumar Murakonda and Reza Shokri. 2020. ML Privacy Meter: Aiding regulatory compliance by quantifying the privacy risks of machine learning. arXiv preprint arXiv:2007.09339 (2020)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243855"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00065"},{"key":"e_1_3_2_1_38_1","volume-title":"Realcause: Realistic causal inference benchmarking. arXiv preprint arXiv:2011.15007","author":"Neal Brady","year":"2020","unstructured":"Brady Neal , Chin-Wei Huang , and Sunand Raghupathi . 2020 . Realcause: Realistic causal inference benchmarking. arXiv preprint arXiv:2011.15007 (2020). Brady Neal, Chin-Wei Huang, and Sunand Raghupathi. 2020. 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Regulation (EU) 2016\/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95\/46\/EC (General Data Protection Regulation). , Vol. L119 (2016), 1--88."},{"key":"e_1_3_2_1_41_1","volume-title":"PyTorch: An Imperative Style","author":"Paszke Adam","unstructured":"Adam Paszke , Sam Gross , Francisco Massa , Adam Lerer , James Bradbury , Gregory Chanan , Trevor Killeen , Zeming Lin , Natalia Gimelshein , Luca Antiga , Alban Desmaison , Andreas Kopf , Edward Yang , Zachary DeVito , Martin Raison , Alykhan Tejani , Sasank Chilamkurthy , Benoit Steiner , Lu Fang , Junjie Bai , and Soumith Chintala . 2019. PyTorch: An Imperative Style , High-Performance Deep Learning Library . In Advances in Neural Information Processing Systems (NeurIPS), , H. Wallach, H. Larochelle, A. Beygelzimer, F. dtextquotesingle Alch\u00e9-Buc, E. Fox, and R. Garnett (Eds.). Curran Associates, Inc., 8024--8035. http:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems (NeurIPS), , H. Wallach, H. Larochelle, A. Beygelzimer, F. dtextquotesingle Alch\u00e9-Buc, E. Fox, and R. Garnett (Eds.). Curran Associates, Inc., 8024--8035. http:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf"},{"key":"e_1_3_2_1_42_1","unstructured":"Judea Pearl. 2009. Causality. Cambridge university press.  Judea Pearl. 2009. Causality. Cambridge university press."},{"key":"e_1_3_2_1_43_1","volume-title":"On a class of bias-amplifying variables that endanger effect estimates. arXiv preprint arXiv:1203.3503","author":"Pearl Judea","year":"2012","unstructured":"Judea Pearl . 2012. On a class of bias-amplifying variables that endanger effect estimates. arXiv preprint arXiv:1203.3503 ( 2012 ). Judea Pearl. 2012. On a class of bias-amplifying variables that endanger effect estimates. arXiv preprint arXiv:1203.3503 (2012)."},{"key":"e_1_3_2_1_44_1","volume-title":"Cambridge, UK: CambridgeUniversityPress","author":"Judea Pearl","year":"2000","unstructured":"Judea Pearl et al. 2000 . Models , reasoning and inference. Cambridge, UK: CambridgeUniversityPress , Vol. 19 (2000), 2. Judea Pearl et al. 2000. Models, reasoning and inference. Cambridge, UK: CambridgeUniversityPress , Vol. 19 (2000), 2."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-1284-3_1"},{"key":"e_1_3_2_1_46_1","volume-title":"Combinatorial mathematics","author":"Robinson Robert W","unstructured":"Robert W Robinson . 1977. Counting unlabeled acyclic digraphs . In Combinatorial mathematics V. Springer , 28--43. Robert W Robinson. 1977. Counting unlabeled acyclic digraphs. In Combinatorial mathematics V. Springer, 28--43."},{"key":"e_1_3_2_1_47_1","volume-title":"Sensitivity analysis in observational studies. Encyclopedia of statistics in behavioral science","author":"Rosenbaum Paul R","year":"2005","unstructured":"Paul R Rosenbaum . 2005. Sensitivity analysis in observational studies. Encyclopedia of statistics in behavioral science ( 2005 ). Paul R Rosenbaum. 2005. Sensitivity analysis in observational studies. Encyclopedia of statistics in behavioral science (2005)."},{"key":"e_1_3_2_1_48_1","volume-title":"International Conference on Machine Learning (ICML). PMLR, 5558--5567","author":"Sablayrolles Alexandre","year":"2019","unstructured":"Alexandre Sablayrolles , Matthijs Douze , Cordelia Schmid , Yann Ollivier , and Herv\u00e9 J\u00e9gou . 2019 . White-box vs black-box: Bayes optimal strategies for membership inference . In International Conference on Machine Learning (ICML). PMLR, 5558--5567 . Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, and Herv\u00e9 J\u00e9gou. 2019. White-box vs black-box: Bayes optimal strategies for membership inference. In International Conference on Machine Learning (ICML). PMLR, 5558--5567."},{"key":"e_1_3_2_1_49_1","volume-title":"ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models. In Network and Distributed Systems Security Symposium (NDSS). Internet Society.","author":"Salem Ahmed","year":"2019","unstructured":"Ahmed Salem , Yang Zhang , Mathias Humbert , Mario Fritz , and Michael Backes . 2019 . ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models. In Network and Distributed Systems Security Symposium (NDSS). Internet Society. Ahmed Salem, Yang Zhang, Mathias Humbert, Mario Fritz, and Michael Backes. 2019. ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models. In Network and Distributed Systems Security Symposium (NDSS). Internet Society."},{"key":"e_1_3_2_1_50_1","volume-title":"Estimating the dimension of a model. The annals of statistics","author":"Schwarz Gideon","year":"1978","unstructured":"Gideon Schwarz . 1978. Estimating the dimension of a model. The annals of statistics ( 1978 ), 461--464. Gideon Schwarz. 1978. Estimating the dimension of a model. The annals of statistics (1978), 461--464."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v035.i03"},{"key":"e_1_3_2_1_52_1","volume-title":"On Identifying Significant Edges in Graphical Models of Molecular Networks. arXiv preprint arXiv:1104.0896","author":"Scutari Marco","year":"2011","unstructured":"Marco Scutari and Radhakrishnan Nagarajan . 2011. On Identifying Significant Edges in Graphical Models of Molecular Networks. arXiv preprint arXiv:1104.0896 ( 2011 ). Marco Scutari and Radhakrishnan Nagarajan. 2011. On Identifying Significant Edges in Graphical Models of Molecular Networks. arXiv preprint arXiv:1104.0896 (2011)."},{"key":"e_1_3_2_1_53_1","first-page":"3195","article-title":"Learning Causal Graphs with Small Interventions","volume":"28","author":"Shanmugam Karthikeyan","year":"2015","unstructured":"Karthikeyan Shanmugam , Murat Kocaoglu , Alexandros G Dimakis , and Sriram Vishwanath . 2015 . Learning Causal Graphs with Small Interventions . 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DoWhy: A Python package for causal inference. https:\/\/github.com\/microsoft\/dowhy.  Amit Sharma Emre Kiciman et al. 2019. DoWhy: A Python package for causal inference. https:\/\/github.com\/microsoft\/dowhy."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3461702.3462533"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"e_1_3_2_1_58_1","volume-title":"Proceedings of the National Conference on Artificial Intelligence","volume":"21","author":"Shpitser Ilya","year":"2006","unstructured":"Ilya Shpitser and Judea Pearl . 2006 . Identification of joint interventional distributions in recursive semi-Markovian causal models . In Proceedings of the National Conference on Artificial Intelligence , Vol. 21 . Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999, 1219. Ilya Shpitser and Judea Pearl. 2006. Identification of joint interventional distributions in recursive semi-Markovian causal models. In Proceedings of the National Conference on Artificial Intelligence, Vol. 21. Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999, 1219."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1951.tb00088.x"},{"key":"e_1_3_2_1_60_1","volume-title":"USENIX Security Symposium.","author":"Song Liwei","year":"2021","unstructured":"Liwei Song and Prateek Mittal . 2021 . Systematic evaluation of privacy risks of machine learning models . In USENIX Security Symposium. Liwei Song and Prateek Mittal. 2021. Systematic evaluation of privacy risks of machine learning models. In USENIX Security Symposium."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/SPW.2019.00021"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3319535.3354211"},{"key":"e_1_3_2_1_63_1","volume-title":"International Conference on Machine Learning (ICML). PMLR, 9537--9547","author":"Tople Shruti","year":"2020","unstructured":"Shruti Tople , Amit Sharma , and Aditya Nori . 2020 . Alleviating privacy attacks via causal learning . In International Conference on Machine Learning (ICML). PMLR, 9537--9547 . Shruti Tople, Amit Sharma, and Aditya Nori. 2020. Alleviating privacy attacks via causal learning. In International Conference on Machine Learning (ICML). PMLR, 9537--9547."},{"key":"e_1_3_2_1_64_1","volume-title":"Predicting causal relationships from biological data: Applying automated causal discovery on mass cytometry data of human immune cells. Scientific reports","author":"Triantafillou Sofia","year":"2017","unstructured":"Sofia Triantafillou , Vincenzo Lagani , Christina Heinze-Deml , Angelika Schmidt , Jesper Tegner , and Ioannis Tsamardinos . 2017. Predicting causal relationships from biological data: Applying automated causal discovery on mass cytometry data of human immune cells. Scientific reports , Vol. 7 , 1 ( 2017 ), 1--11. Sofia Triantafillou, Vincenzo Lagani, Christina Heinze-Deml, Angelika Schmidt, Jesper Tegner, and Ioannis Tsamardinos. 2017. Predicting causal relationships from biological data: Applying automated causal discovery on mass cytometry data of human immune cells. Scientific reports, Vol. 7, 1 (2017), 1--11."},{"key":"e_1_3_2_1_65_1","volume-title":"Lei Yu, and Wenqi Wei.","author":"Truex Stacey","year":"2019","unstructured":"Stacey Truex , Ling Liu , Mehmet Emre Gursoy , Lei Yu, and Wenqi Wei. 2019 . Demystifying membership inference attacks in machine learning as a service. IEEE Transactions on Services Computing ( 2019). Stacey Truex, Ling Liu, Mehmet Emre Gursoy, Lei Yu, and Wenqi Wei. 2019. Demystifying membership inference attacks in machine learning as a service. IEEE Transactions on Services Computing (2019)."},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP40000.2020.00012"},{"key":"e_1_3_2_1_67_1","volume-title":"Proc. of World Academy of Science, Engineering and Technology","volume":"34","author":"Wu Huihai","year":"2008","unstructured":"Huihai Wu and Xiaohui Liu . 2008 . Dynamic bayesian networks modeling for inferring genetic regulatory networks by search strategy: Comparison between greedy hill climbing and mcmc methods . In Proc. of World Academy of Science, Engineering and Technology , Vol. 34 . Citeseer, 224--234. Huihai Wu and Xiaohui Liu. 2008. Dynamic bayesian networks modeling for inferring genetic regulatory networks by search strategy: Comparison between greedy hill climbing and mcmc methods. In Proc. of World Academy of Science, Engineering and Technology, Vol. 34. Citeseer, 224--234."},{"key":"e_1_3_2_1_68_1","volume-title":"International Conference on Machine Learning (ICML). 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