causal.bib

@inproceedings{AcharyaBDK18,
  title     = {Learning and Testing Causal Models with Interventions},
  author    = {Acharya, Jayadev and Bhattacharyya, Arnab and Daskalakis, Constantinos and Kandasamy, Saravanan},
  booktitle = {Advances in Neural Information Processing Systems 31},
  pages     = {9447--9460},
  year      = {2018},
  url       = {http://papers.nips.cc/paper/8155-learning-and-testing-causal-models-with-interventions.html},
}
@article{AngristIR96,
  title     = {Identification of Causal Effects Using Instrumental Variables (with discussion)},
  author    = {Joshua D. Angrist and Guido W. Imbens and Donald B. Rubin},
  journal   = {Journal of the American Statistical Association},
  volume    = {91},
  number    = {434},
  pages     = {444--455},
  year      = {1996},
  url       = {https://www.tandfonline.com/doi/abs/10.1080/01621459.1996.10476902},
}
@inproceedings{AlaaSchaar19,
  title     = {Validating Causal Inference Models via Influence Functions},
  author    = {Alaa, Ahmed and {van} Der Schaar, Mihaela},
  booktitle = {Proceedings of the 36th International Conference on Machine Learning},
  pages     = {191--201},
  year      = {2019},
  url       = {http://proceedings.mlr.press/v97/alaa19a.html},
}
@inproceedings{AndrewsSC20,
  title     = {On the Completeness of Causal Discovery in the Presence of Latent Confounding with Tiered Background Knowledge},
  author    = {Andrews, Bryan and Spirtes, Peter and Cooper, Gregory F.},
  booktitle = {Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics},  
  pages     = {4002--4011},
  year      = {2020},
  url       = {http://proceedings.mlr.press/v108/andrews20a.html},
}
@article{BackhoffBLZ17,
  title     = {Causal Transport in Discrete Time and Applications},
  author    = {Backhoff, Julio and Beiglb{\"o}ck, Mathias and Lin, Yiqing and Zalashko, Anastasiia},
  journal   = {{SIAM} Journal on Optimization},
  volume    = {27},
  number    = {4},
  pages     = {2528--2562},
  year      = {2017},
  url       = {https://doi.org/10.1137/16M1080197},
  keywords  = {journal},
}
@article {BareinboimPearl16,
  title     = {Causal inference and the data-fusion problem},
  author    = {Bareinboim, Elias and Pearl, Judea},
  journal   = {Proceedings of the National Academy of Sciences},
  volume    = {113},
  number    = {27},
  pages     = {7345--7352},
  year      = {2016},
  url       = {https://www.pnas.org/content/113/27/7345},
}
@inproceedings{BengioDRKLBGP20,
  title     = {A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms},
  author    = {Yoshua Bengio and Tristan Deleu and Nasim Rahaman and Nan Rosemary Ke and Sebastien Lachapelle and Olexa Bilaniuk and Anirudh Goyal and Christopher Pal},
  booktitle = {International Conference on Learning Representations},
  year      = {2020},
  url       = {https://openreview.net/forum?id=ryxWIgBFPS},
}
@inproceedings{BesserveSSJ18,
  title     = {Group invariance principles for causal generative models},
  author    = {Michel Besserve and Naji Shajarisales and Bernhard Sch{\"o}lkopf and Dominik Janzing},
  booktitle = {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics},
  pages     = {557--565},
  year      = {2018},
  url       = {http://proceedings.mlr.press/v84/besserve18a.html},
}
@inproceedings{BesserveMSS20,
  title     = {Counterfactuals uncover the modular structure of deep generative models},
  author    = {Michel Besserve and Arash Mehrjou and R{\'e}my Sun and Bernhard Sch{\"o}lkopf},
  booktitle = {International Conference on Learning Representation},
  year      = {2020},
  url       = {https://iclr.cc/virtual_2020/poster_SJxDDpEKvH.html},
}
@article{BottouPCCCPRSS13,
  author    = {L{\'e}on Bottou and Jonas Peters and Joaquin Qui{\~n}onero-Candela and Denis X. Charles and D. Max Chickering and Elon Portugaly and Dipankar Ray and Patrice Simard and Ed Snelson},
  title     = {Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising},
  journal   = {Journal of Machine Learning Research},
  year      = {2013},
  volume    = {14},
  number    = {65},
  pages     = {3207--3260},
  url       = {http://jmlr.org/papers/v14/bottou13a.html},
}
@inproceedings{CelisMV20,
  title     = {Interventions for Ranking in the Presence of Implicit Bias},
  author    = {Celis, L. Elisa and Mehrotra, Anay and Vishnoi, Nisheeth K.},
  booktitle = {Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency},
  pages     = {369–-380},
  year      = {2020},
  url       = {https://doi.org/10.1145/3351095.3372858},
}
@article{ChernozhukovFM13,
  title 	  = {Inference on Counterfactual Distributions},
  author 	  = {Chernozhukov, Victor and Fern{\'a}ndez-Val, Iv{\'a}n and Melly, Blaise},
  journal 	= {Econometrica},
  volume 	  = {81},
  number 	  = {6},
  pages 	  = {2205--2268},
  year 		  = {2013},
  url 		  = {https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA10582},
}
@inproceedings{CreagerMPZ20,
  title     = {Causal Modeling for Fairness In Dynamical Systems},
  author    = {Elliot Creager and David Madras and Toniann Pitassi and Richard Zemel},
  booktitle = {Proceedings of the 37th International Conference on Machine Learning},
  year      = {2020},
  url       = {https://icml.cc/virtual/2020/poster/6548},
}
@inproceedings{DAmour19,
  title     = {On Multi-Cause Approaches to Causal Inference with Unobserved Counfounding: Two Cautionary Failure Cases and A Promising Alternative},
  author    = {D'Amour, Alexander},
  booktitle = {Proceedings of the 36th International Conference on Machine Learning},
  pages     = {3478--3486},
  year      = {2019},
  url       = {http://proceedings.mlr.press/v89/d-amour19a.html},
}
@article{Dawid79,
  title     = {Conditional Independence in Statistical Theory},
  author    = {A. Philip Dawid},
  journal   = {Journal of the Royal Statistical Society. Series B (Methodological)},
  volume    = {41},
  number    = {1},
  pages     = {1--31},
  year      = {1979},
  url       = {http://www.jstor.org/stable/2984718},
}
@article{Dawid80,
  title     = {Conditional Independence for Statistical Operations},
  author    = {A. Philip Dawid},
  journal   = {The Annals of Statistics},
  volume    = {8},
  number    = {3},
  pages     = {598--617},
  year      = {1980},
  url       = {http://www.jstor.org/stable/2240595},  
}
@article{Dawid00,
  title     = {Causal Inference Without Counterfactuals (with discussion)},
  author    = {A. Philip Dawid},
  journal   = {Journal of the American Statistical Association},
  volume    = {95},
  number    = {450},
  pages     = {407--424},
  year      = {2000},
  url       = {http://www.jstor.org/stable/2669377},
}
@article{Dawid15,
  title     = {Statistical Causality from a Decision-Theoretic Perspective},
  author    = {Dawid, A. Philip},
  journal   = {Annual Review of Statistics and Its Application},
  volume    = {2},
  number    = {1},
  pages     = {273--303},
  year      = {2015},
  url       = {https://doi.org/10.1146/annurev-statistics-010814-020105},
  keywords  = {review},
}
@inproceedings{DingGZT19,
  title 	  = {Likelihood-Free Overcomplete ICA and Applications In Causal Discovery},
  author 	  = {Ding, Chenwei and Gong, Mingming and Zhang, Kun and Tao, Dacheng},
  booktitle = {Advances in Neural Information Processing Systems 32},
  pages 	  = {6883--6893},
  year 		  = {2019},
  url 		  = {http://papers.nips.cc/paper/8912-likelihood-free-overcomplete-ica-and-applications-in-causal-discovery.html},
}
@article{DingLi18,
  title     = {Causal Inference: A Missing Data Perspective},
  author    = {Ding, Peng and Li, Fan},
  journal   = {Statistical Science},
  volume    = {33},
  number    = {2},
  pages     = {214--237},
  year      = {2018},
  url       = {https://doi.org/10.1214/18-STS645},
}
@article{EfronFeldman91,
  title     = {Compliance as an Explanatory Variable in Clinical Trials (with discussion)},
  author    = {B. Efron and D. Feldman},
  journal   = {Journal of the American Statistical Association},
  volume    = {86},
  number    = {413},
  pages     = {9--17},
  year      = {1991},
  url       = {http://www.jstor.org/stable/2289707},
}
@article{FrangakisRubin02,
  title     = {Principal Stratification in Causal Inference},
  author    = {Frangakis, Constantine E. and Rubin, Donald B.},
  journal   = {Biometrics},
  volume    = {58},
  number    = {1},
  pages     = {21--29},
  url       = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.0006-341X.2002.00021.x},
  year      = {2002},
}
@inproceedings{GargPLTCB19,
  title     = {Counterfactual Fairness in Text Classification through Robustness},
  author    = {Garg, Sahaj and Perot, Vincent and Limtiaco, Nicole and Taly, Ankur and Chi, Ed H. and Beutel, Alex},
  booktitle = {Proceedings of the 2019 {AAAI/ACM} Conference on {AI}, Ethics, and Society},
  pages     = {219–-226},
  year      = {2019},
  url       = {https://doi.org/10.1145/3306618.3317950},
}
@article{GengLLM19,
  title     = {Evaluation of Causal Effects and Local Structure Learning of Causal Networks},
  author    = {Geng, Zhi and Liu, Yue and Liu, Chunchen and Miao, Wang},
  journal   = {Annual Review of Statistics and Its Application},
  volume    = {6},
  number    = {1},
  pages     = {103--124},
  year      = {2019},
  url       = {https://doi.org/10.1146/annurev-statistics-030718-105312},
  keywords  = {review},
}
@inproceedings{GentzelGJ19,
  title 	  = {The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data},
  author 	  = {Gentzel, Amanda and Garant, Dan and Jensen, David},
  booktitle = {Advances in Neural Information Processing Systems 32},
  pages 	  = {11722--11732},
  year 		  = {2019},
  url 		  = {http://papers.nips.cc/paper/9345-the-case-for-evaluating-causal-models-using-interventional-measures-and-empirical-data.html},
}
@inproceedings{GultchinKKS20,
  title     = {Differentiable Causal Backdoor Discovery},
  author    = {Gultchin, Limor and Kusner, Matt and Kanade, Varun and Silva, Ricardo},
  booktitle = {Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics},  
  pages     = {3970--3979},
  year      = {2020},
  url       = {http://proceedings.mlr.press/v108/gultchin20a.html},
}
@article{Heckman89,
  title     = {Causal Inference and Nonrandom Samples},
  author    = {James J. Heckman},
  journal   = {Journal of Educational Statistics},
  volume    = {14},
  number    = {2},
  pages     = {159--168},
  year      = {1989},
  url       = {https://doi.org/10.3102/10769986014002159},
}
@article{HeinzeDemlMM18,
  title     = {Causal Structure Learning},
  author    = {Heinze-Deml, Christina and Maathuis, Marloes H. and Meinshausen, Nicolai},
  journal   = {Annual Review of Statistics and Its Application},
  volume    = {5},
  number    = {1},
  pages     = {371--391},
  year      = {2018},
  url       = {https://doi.org/10.1146/annurev-statistics-031017-100630},
  keywords  = {review},
}
@article{Holland86,
  title     = {Statistics and Causal Inference (with discussion)},
  author    = {Paul W. Holland},
  journal   = {Journal of the American Statistical Association},
  volume    = {81},
  number    = {396},
  pages     = {945--960},
  year      = {1986},
  url       = {https://www.tandfonline.com/doi/abs/10.1080/01621459.1986.10478354},
}
@article{HuangValtorta08,
  title     = {On the completeness of an identifiability algorithm for semi-Markovian models},
  author    = {Yimin Huang and Marco Valtorta},
  journal   = {Annals of Mathematics and Artificial Intelligence},
  volume    = {54},
  pages     = {363-–408},
  year      = {2008},
  url       = {https://doi.org/10.1007/s10472-008-9101-x},
}
@inproceedings{JaberZB19,
  title 	  = {Identification of Conditional Causal Effects under Markov Equivalence},
  author 	  = {Jaber, Amin and Zhang, Jiji and Bareinboim, Elias},
  booktitle = {Advances in Neural Information Processing Systems 32},
  pages 	  = {11516--11524},
  year 		  = {2019},
  url 		  = {http://papers.nips.cc/paper/9327-identification-of-conditional-causal-effects-under-markov-equivalence.html},
}
@inproceedings{Janzing19,
  title 	  = {Causal Regularization},
  author 	  = {Janzing, Dominik},
  booktitle = {Advances in Neural Information Processing Systems 32},
  pages 	  = {12704--12714},
  year 		  = {2019},
  url 		  = {http://papers.nips.cc/paper/9432-causal-regularization.html},
}
@inproceedings{JanzingMB20,
  title     = {Feature relevance quantification in explainable AI: A causal problem},
  author    = {Janzing, Dominik and Minorics, Lenon and Bloebaum, Patrick},
  booktitle = {Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics},  
  pages     = {2907--2916},
  year      = {2020},
  url       = {http://proceedings.mlr.press/v108/janzing20a.html},
}
@inproceedings{JohanssonSS16,
  title     = {Learning Representations for Counterfactual Inference},
  author    = {Fredrik Johansson and Uri Shalit and David Sontag},
  booktitle = {Proceedings of The 33rd International Conference on Machine Learning},
  pages     = {3020--3029},
  year      = {2016},
  url       = {http://proceedings.mlr.press/v48/johansson16.html},
}
@inproceedings{KallusMU18,
  title     = {Causal Inference with Noisy and Missing Covariates via Matrix Factorization},
  author    = {Kallus, Nathan and Mao, Xiaojie and Udell, Madeleine},
  booktitle = {Advances in Neural Information Processing Systems 31},
  pages     = {6921--6932},
  year      = {2018},
  url       = {http://papers.nips.cc/paper/7924-causal-inference-with-noisy-and-missing-covariates-via-matrix-factorization.html},
}
@inproceedings{Kallus20,
  title     = {DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training},
  author    = {Nathan Kallus},
  booktitle = {Proceedings of the 37th International Conference on Machine Learning},
  year      = {2020},
  url       = {https://icml.cc/virtual/2020/poster/6247},
}
@inproceedings{KarimiBBV20,
  title     = {Model-Agnostic Counterfactual Explanations for Consequential Decisions},
  author    = {Karimi, Amir-Hossein and Barthe, Gilles and Balle, Borja and Valera, Isabel},
  booktitle = {Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics},  
  pages     = {895--905},
  year      = {2020},
  url       = {http://proceedings.mlr.press/v108/karimi20a.html},
}
@inproceedings{KaushikHL20,
  title     = {Learning The Difference That Makes A Difference With Counterfactually-Augmented Data},
  author    = {Divyansh Kaushik and Eduard Hovy and Zachary Lipton},
  booktitle = {International Conference on Learning Representation},
  year      = {2020},
  url       = {https://iclr.cc/virtual_2020/poster_Sklgs0NFvr.html},
}
@article{KennedyMDBS19,
  title     = {Handling Missing Data in Instrumental Variable Methods for Causal Inference},
  author    = {Kennedy, Edward H. and Mauro, Jacqueline A. and Daniels, Michael J. and Burns, Natalie and Small, Dylan S.},
  journal   = {Annual Review of Statistics and Its Application},
  volume    = {6},
  number    = {1},
  pages     = {125--148},
  year      = {2019},
  url       = {https://doi.org/10.1146/annurev-statistics-031017-100353},
  keywords  = {review},
}
@inproceedings{KilbertusRPHJS17,
  title     = {Avoiding Discrimination through Causal Reasoning},
  author    = {Kilbertus, Niki and Rojas Carulla, Mateo and Parascandolo, Giambattista and Hardt, Moritz and Janzing, Dominik and Sch\"{o}lkopf, Bernhard},
  booktitle = {Advances in Neural Information Processing Systems 30},
  pages     = {656--666},
  year      = {2017},
  url       = {http://papers.nips.cc/paper/6668-avoiding-discrimination-through-causal-reasoning.html},
}
@inproceedings{KocaogluSDV18,
  title     = {Causal{GAN}: Learning Causal Implicit Generative Models with Adversarial Training},
  author    = {Murat Kocaoglu and Christopher Snyder and Alexandros G. Dimakis and Sriram Vishwanath},
  booktitle = {International Conference on Learning Representations},
  year      = {2018},
  url       = {https://openreview.net/forum?id=BJE-4xW0W},
}
@inproceedings{KocaogluJSB19,
  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},
  booktitle = {Advances in Neural Information Processing Systems 32},
  pages 	  = {14369--14379},
  year 		  = {2019},
  url 		  = {http://papers.nips.cc/paper/9581-characterization-and-learning-of-causal-graphs-with-latent-variables-from-soft-interventions.html},
}
@article{KohlerKS19,
  title     = {Nonprobability Sampling and Causal Analysis},
  author    = {Kohler, Ulrich and Kreuter, Frauke and Stuart, Elizabeth A.},
  journal   = {Annual Review of Statistics and Its Application},
  volume    = {6},
  number    = {1},
  pages     = {149--172},
  year      = {2019},
  url       = {https://doi.org/10.1146/annurev-statistics-030718-104951},
  keywords  = {review},
}
@inproceedings{KuangSSWCDPR20,
  title     = {Ivy: Instrumental Variable Synthesis for Causal Inference},
  author    = {Kuang, Zhaobin and Sala, Frederic and Sohoni, Nimit and Wu, Sen and C{\'o}rdova-Palomera, Aldo and Dunnmon, Jared and Priest, James and Re, Christopher},
  booktitle = {Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics},  
  pages     = {398--410},
  year      = {2020},
  url       = {http://proceedings.mlr.press/v108/kuang20a.html},
}
@inproceedings{KusnerSSW16,
  title     = {Private Causal Inference},
  author    = {Matt J. Kusner and Yu Sun and Karthik Sridharan and Kilian Q. Weinberger},
  booktitle = {Proceedings of the 19th International Conference on Artificial Intelligence and Statistics},
  pages     = {1308--1317},
  year      = {2016},
  url       = {http://proceedings.mlr.press/v51/kusner16.html},
}
@inproceedings{KusnerLRS17,
  title     = {Counterfactual Fairness},
  author    = {Kusner, Matt J and Loftus, Joshua and Russell, Chris and Silva, Ricardo},
  booktitle = {Advances in Neural Information Processing Systems 30},
  pages     = {4066--4076},
  year      = {2017},
  url       = {http://papers.nips.cc/paper/6995-counterfactual-fairness.html},
}
@inproceedings{LiptonMC18,
  title     = {Does mitigating ML's impact disparity require treatment disparity?},
  author    = {Lipton, Zachary and McAuley, Julian and Chouldechova, Alexandra},
  booktitle = {Advances in Neural Information Processing Systems 31},
  pages     = {8125--8135},
  year      = {2018},
  url       = {http://papers.nips.cc/paper/8035-does-mitigating-mls-impact-disparity-require-treatment-disparity.html},
}
@article{Little21,
  title     = {Missing Data Assumptions},
  author    = {Little, Roderick J.},
  journal   = {Annual Review of Statistics and Its Application},
  volume    = {8},
  number    = {1},
  year      = {2021},
  url       = {https://doi.org/10.1146/annurev-statistics-040720-031104},
  keywords  = {review},
}
@article{LittleRubin00,
  title     = {Causal Effects in Clinical and Epidemiological Studies Via Potential Outcomes: Concepts and Analytical Approaches},
  author    = {Little, Roderick J. and Rubin, Donald B.},
  journal   = {Annual Review of Public Health},
  volume    = {21},
  number    = {1},
  pages     = {121--145},
  url       = {https://doi.org/10.1146/annurev.publhealth.21.1.121},
  year      = {2000},
}
@inproceedings{MadrasCPZ19,
  title     = {Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data},
  author    = {Madras, David and Creager, Elliot and Pitassi, Toniann and Zemel, Richard},
  booktitle = {Proceedings of the Conference on Fairness, Accountability, and Transparency},
  pages     = {349–-358},
  year      = {2019},
  url       = {https://doi.org/10.1145/3287560.3287564},
}
@inproceedings{MillerMH20,
  title     = {Strategic Classification is Causal Modeling in Disguise},
  author    = {John Miller and Smitha Milli and Moritz Hardt},
  booktitle = {Proceedings of the 37th International Conference on Machine Learning},
  year      = {2020},
  url       = {https://icml.cc/virtual/2020/poster/6779},
}
@inproceedings{MitrovicST18,
  title     = {Causal Inference via Kernel Deviance Measures},
  author    = {Mitrovic, Jovana and Sejdinovic, Dino and Teh, Yee Whye},
  booktitle = {Advances in Neural Information Processing Systems 31},
  pages     = {6986--6994},
  year      = {2018},
  url       = {http://papers.nips.cc/paper/7930-causal-inference-via-kernel-deviance-measures.html},
}
@inproceedings{NessPV19,
  title 	  = {Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems},
  author  	= {Ness, Robert and Paneri, Kaushal and Vitek, Olga},
  booktitle = {Advances in Neural Information Processing Systems 32},
  pages 	  = {14234--14244},
  year 		  = {2019},
  url 		  = {http://papers.nips.cc/paper/9569-integrating-markov-processes-with-structural-causal-modeling-enables-counterfactual-inference-in-complex-systems.html},
}
@article{NeymanDS90,
  title     = {On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9.},
  author    = {Jerzy Splawa-Neyman and D. M. Dabrowska and T. P. Speed},
  journal   = {Statistical Science},
  number    = {4},
  pages     = {465--472},
  volume    = {5},
  year      = {1990},
  url       = {http://www.jstor.org/stable/2245382},
}
@inproceedings{ParascandoloKRCS18,
  title     = {Learning Independent Causal Mechanisms},
  author    = {Parascandolo, Giambattista and Kilbertus, Niki and Rojas-Carulla, Mateo and Sch{\"o}lkopf, Bernhard},
  booktitle = {Proceedings of the 35th International Conference on Machine Learning},
  pages     = {4036--4044},
  year      = {2018},
  url       = {http://proceedings.mlr.press/v80/parascandolo18a.html},
}
@article{Pearl95,
  title     = {Causal diagrams for empirical research},
  author    = {Pearl, Judea},
  journal   = {Biometrika},
  volume    = {82},
  number    = {4},
  pages     = {669--688},
  year      = {1995},
  url       = {https://doi.org/10.1093/biomet/82.4.669},
}
@inproceedings{PearlRobins95,
  title     = {Probabilistic evaluation of sequential plans from causal models with hidden variables},
  author    = {Pearl, Judea and James Robins},
  booktitle = {Proceedings of the Eleventh conference on Uncertainty in artificial intelligence},
  pages     = {444–-453},
  year      = {1995},
  url       = {https://dl.acm.org/doi/10.5555/2074158.2074209},
}
@inproceedings{Pearl01,
  title     = {Direct and Indirect Effects},
  author    = {Pearl, Judea},
  booktitle = {Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence},
  pages     = {411--420},
  year      = {2001},
  url       = {https://dl.acm.org/doi/10.5555/2074022.2074073},
}
@article{Pearl09,
  title     = {Causal inference in statistics: An overview},
  author    = {Pearl, Judea},
  journal   = {Statistics Surveys},
  volume    = {3},
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