Related reviews

Related reviews and papers

Theory

  • NEW P. Upadhyaya, K. Zhang, C. Li, X. Jiang, Y. Kim. Scalable Causal Structure Learning: New Opportunities in Biomedicine. Arxiv preprint arXiv:2110.07785, 2021.
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  • M. J. Vowels, N. C. Camgoz, R. Bowden. D'ya like DAGs? A Survey on Structure Learning and Causal Discovery. Arxiv preprint arXiv:2103.02582, 2021.
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  • B. Schölkopf, F. Locatello, S. Bauer, N. R. Ke, N. Kalchbrenner, A. Goyal, Y. Bengio. Towards Causal Representation Learning. Arxiv preprint arXiv:2102.11107, 2021.
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  • R. Moraffah, P. Sheth, M. Karami, A. Bhattacharya, Q. Wang, A. Tahir, A. Raglin, H. Liu. Causal Inference for Time series Analysis: Problems, Methods and Evaluation. Arxiv preprint arXiv:2102.05829, 2021.
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  • S. Shimizu, P. Blöbaum. Recent Advances in Semi-Parametric Methods for Causal Discovery. Direction Dependence in Statistical Modeling: Methods of Analysis, pp. xx-xx, 2020.
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  • Y. Luo, J. Peng, and J. Ma. When causal inference meets deep learning. Nature Machine Intelligence volume, 2, pp. 426-427, 2020.
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  • H. A. Abdulkadhim, M. S. Ibrahim, A. N. Albu-Rghaif. Autoregressive Models of the Random fields—A Survey. Iraqi Journal of Computers, Communication, Control & Systems Engineering, 20(2): xx-xx, pp. xx-xx, 2020.
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  • K. Kuang, L. Li, Z. Geng, L, Xu, K. Zhang, B. Liao, H. Huang, P. Ding, W. Miao, Z. Jiangi. Causal Inference. Engineering, xx(xx): xx-xx, pp. xx-xx, 2020.
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  • F. Eberhardt. Beyond Cause-Effect Pairs. Cause Effect Pairs in Machine Learning, pp. 215-233, 2019.
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  • D. Janzing. The Cause-Effect Problem: Motivation, Ideas, and Popular Misconceptions. Cause Effect Pairs in Machine Learning, pp. 3-26, 2019.
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  • I. Guyon, O. Goudet, D. Kalainathan. Evaluation Methods of Cause-Effect Pairs. Cause Effect Pairs in Machine Learning, pp. 27-99, 2019.
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  • O. Goudet, D. Kalainathan, M. Sebag, I.Guyon. Learning Bivariate Functional Causal Models. Cause Effect Pairs in Machine Learning, pp. 101-153, 2019.
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  • N. Doremus, A. Moneta, S Cattaruzzo. Cause-Effect Pairs in Time Series with a Focus on Econometrics. Cause Effect Pairs in Machine Learning, pp. 191-214, 2019.
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  • C. Glymour, K. Zhang, and P. Spirtes. Review of Causal Discovery Methods Based on Graphical Models. Frontiers in Genetics, xx(xx): xx-xx, 2019.
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  • N. Climenhaga, L. DesAutels, G. Ramsey. Causal Inference from Noise. Noûs, xx(xx): xx-xx, 2019.
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  • J. Pearl. The seven tools of causal inference with reflections on machine learning. Communications of the ACM, 62(3), 54-60, 2019.
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  • B. Schölkopf. CAUSALITY FOR MACHINE LEARNING. Arxiv preprint arXiv:1911.10500, 2019.
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  • J. Runge. Causal network reconstruction from time series: From theoretical assumptions to practical estimation. Chaos, 28: 075310, 2018.
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  • S. Shimizu. Non-Gaussian methods for causal structure learning. Prevention Science, xx--xx, 2018.
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  • C. Heinze-Deml, M. H. Maathuis, and N. Meinshausen. Causal structure learning. Arxiv preprint arXiv:1706.09141, 2017.
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  • J. Peters, D. Janzing, and B. Schölkopf. Elements of causal inference: foundations and learning algorithms. MIT Press, 2017.
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  • K. Zhang, B. Schölkopf, P. Spirtes, and C. Glymour. Learning causality and causality-related learning: Some recent progress. National Science Review, nwx137, 2017.
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  • D. Janzing. Statistical asymmetries between cause and effect. Time in Physics, 129--139, 2017.
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  • M. Drton and M. H. Maathuis. Structure learning in graphical modeling. Arxiv preprint arXiv:1606.02359, 2016.
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  • F. Eberhardt. Introduction to the foundations of causal discovery. International Journal of Data Science and Analytics, xx(xx): xx--xx, 2016.
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  • K. Chalupka, F. Eberhardt, and P. Perona. Causal feature learning: an overview. Behaviormetrika, 44(1): 137–164, 2017. (Special Feature on Recent Developments in Causal Discovery and Inference)
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  • K. Zhang and A. Hyvärinen. Nonlinear functional causal models for distinguishing cause from effect. Statistics and Causality: Methods for Applied Empirical Research, pp. 185-202, 2016.
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  • S. Shimizu. Non-Gaussian structural equation models for causal discovery. Statistics and Causality: Methods for Applied Empirical Research, pp. 153-184, 2016.
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  • W. Wiedermann and A. von Eye. Directionality of effects in causal mediation analysis. Statistics and Causality: Methods for Applied Empirical Research, pp. 63-106, 2016.
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  • P. Spirtes and K. Zhang. Causal discovery and inference: concepts and recent methodological advances. Applied Informatics, xx(xx): xx--xx, 2016.
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  • P. K. Parida, T. Marwala, and S. Chakraverty. An overview of recent advancements in causal studies. Archives of Computational Methods in Engineering, xx(xx): xx--xx, 2016.
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  • M. H. Maathuis and P. Nandy. A review of some recent advances in causal inference. Handbook of Big Data, pp. xx--xx, 2015.
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  • I. Shpitser, R. Evans, T. S. Richardson, and J. M. Robins. Introduction to nested Markov models. Behaviormetrika, 41(1): 3--39, 2014 (Special Issue on Causal Discovery).
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  • R. E. Tillman and F. Eberhardt. Learning causal structure from multiple datasets with similar variable sets. Behaviormetrika, 41(1): 41--64, 2014 (Special Issue on Causal Discovery).
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  • S. Shimizu. LiNGAM: Non-Gaussian methods for estimating causal structures. Behaviormetrika, 41(1): 65--98, 2014 (Special Issue on Causal Discovery).
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  • M. Kalisch and P. Bülhlmann. Causal structure learning and inference: A selective review. Quality Technology & Quantitative Management, 11(1): 3-21, 2014 (Special Issue on 2014 ENBIS-SFdS Spring Meeting Entitled "Graphical Causality Models: Trees, Bayesian Networks and Big Data").
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  • K. Bollen and J. Pearl. Eight myths about causality and structural equation models. Handbook of Causal Analysis for Social Research, pp. 301-328, 2013.
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  • A. Hyvärinen. Independent component analysis: Recent advances. Philosophical Transactions of the Royal Society A, 371: 20110534, 2013.
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  • P. Spirtes, C. Glymour, R. Scheines and R. E. Tillman. Automated search for causal relations: Theory and practice. In Heuristics, Probability and Causality: A Tribute to Judea Pearl, edited by R. Dechter, H. Geffner and J. Halpern, College Publications, pp. 467-506, 2010.
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  • C. Glymour. What is right with ‘Bayes net methods’ and what is wrong with ‘hunting causes and using them’?. British Journal for the Philosophy of Science 61(1): 161--211, 2010.
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  • J. Pearl. Causal inference in statistics: An overview. Statistics Surveys 3: 96--146, 2009.
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  • J. Pearl. Causality: models, reasoning and inference. Cambridge University Press, 2000. (2nd eds. 2009).
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  • D. B. Rubin. Causal inference using potential outcomes. Journal of the American Statistical Association 100(469): 322--331, 2005.
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  • M. A. Hernán. A definition of causal effect for epidemiological research. Journal of Epidemiology & Community Health 58: 265--271, 2004.
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  • P. W. Holland. Statistics and causal inference. Journal of the American Statistical Association 81(396): 945--960, 1986.
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Neuroscience

  • NEW J. Ji, A. Zou, J. Liu, C. Yang, X. Zhang, Y. Song. A Survey on Brain Effective Connectivity Network Learning. IEEE Transactions on Neural Networks, pp. xx--xx, 2021.
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  • N. Z. Bielczyk, S. Uithol, T. van Mourik, P. Anderson, J. C. Glennon,and J. K. Buitelaar. Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches. Network Neuroscience, pp. xx--xx, 2018.
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  • C. Glymour and R. Sanchez-Romero. Underdetermination, behavior and the brain. In The Routledge Handbook of the Computational Mind, pp. xx-xx, 2019. Routledge.
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  • N. Z. Bielczyk, S. Uithol, T. van Mourik, M. N. Havenith, P. Anderson, J. C. Glennon, and J. K. Buitelaar. Causal inference in functional Magnetic Resonance Imaging: a Review of current approaches. Arxiv preprint arXiv:1708.04020, 2017.
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  • T. Henry and K. Gates. Causal search procedures for fMRI: review and suggestions. Behaviormetrika 44(1), 193--225, 2017. (Special Feature on Recent Developments in Causal Discovery and Inference)
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  • C. Glymour and C. Hanson. Reverse inference in neuropsychology. British Journal for the Philosophy of Science, pp. xx--xx, 2015.
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  • L. Faes. Assessing connectivity in the presence of instantaneous causality. In Methods in Brain Connectivity Inference through Multivariate Time Series Analysis, pp. 87--112, 2014. CRC Press.
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  • J. A. Mumford and J. D. Ramsey. Bayesian networks for fMRI: A primer. NeuroImage 86(1): 573--582, 2014.
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  • D. C. Van Essen, K. Ugurbil, E. Auerbach, D. Barch, T. E. J. Behrens, R. Bucholz, A. Chang, L. Chen, M. Corbetta, S. W. Curtiss, S. Della Penna, D. Feinberg, M. F. Glasser, N. Harel, A. C. Heath, L. Larson-Prior, D. Marcus, G. Michalareas, S. Moeller, R. Oostenveld, S. E. Petersen, F. Prior, B. L. Schlaggar, S. M. Smith, A. Z. Snyder,J. Xu, E. Yacoub, WU-Minn HCP Consortium. The human connectome project: A data acquisition perspective. NeuroImage 62(4): 2222--2231, 2012.
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  • S. M. Smith. The future of FMRI connectivity. NeuroImage 62(2): 1257--1266, 2012.
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  • K. E. Stephan and K. J. Friston. System models for inference on mechanisms of neuronal dynamics. Encyclopedia of Molecular Cell Biology and Molecular Medicine, 2012.
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  • J. D. Ramsey, S. J. Hanson, C. Hanson, Y. O. Halchenko, R. A. Poldrack and C. Glymour. Six problems for causal inference from fMRI. NeuroImage 49(2): 1545--1558, 2010.
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Biology/Biomedicine

  • Y. Raita, C. A. Camargo Jr., L. Liang, K. Hasegawa. Leveraging “big data” in respiratory medicine – data science, causal inference, and precision medicine. Expert Review of Respiratory Medicine 15:6, 717-721, 2021.
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  • H. Fröhlich, R. Balling, N. Beerenwinkel, O. Kohlbacher, S. Kumar, T. Lengauer, M. H. Maathuis, Y. Moreau, S. A. Murphy, T. M. Przytycka, M. Rebhan, H. Röst, A. Schuppert, M. Schwab, R. Spang, D. Stekhoven, J. Sun, A. Weber, D. Ziemek and B. Zupan. From hype to reality: data science enabling personalized medicine. BMC Medicine 16:150, 2018.
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  • S. Ma and A. Statnikov. Methods for computational causal discovery in biomedicine. Behaviormetrika 44(1), 165-191, 2017. (Special Feature on Recent Developments in Causal Discovery and Inference)
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  • V. Lagani, S. Triantafillou, G. Ball, J. Tegnér, and I. Tsamardinos. Probabilistic computational causal discovery for systems biology. Uncertainty in Biology, pp. 33--73, 2015.
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  • D. Hurley, H. Araki, Y. Tamada, B. Dunmore, D. Sanders, S. Humphreys, M. Affara, S. Imoto, K. Yasuda, Y. Tomiyasu, K. Tashiro, C. Savoie, V. Cho, S. Smith, S. Kuhara, S. Miyano, D. S. Charnock-Jones, E. J. Crampin and C. G. Print. Gene network inference and visualization tools for biologists: application to new human transcriptome datasets. Nucleic Acids Research 40(6): 2377-2398, 2012.
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  • P. Bühlmann. Causal statistical inference in high dimensions. Mathematical Methods of Operations Research, 77(3): 357-370, 2013.
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  • D. Pe'er and N. Hacohen. Principles and strategies for developing network models in cancer. Cell, 144(6): 864-873, 2011.
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Economics

  • A. Moneta, N. Chlaß, D. Entner and P. O. Hoyer. Causal search in structural vector autoregressive models. In JMLR Workshop and Conference Proceedings, Causality in Time Series (Proc. NIPS2009 Mini-Symposium on Causality in Time Series), 12: 95-118, 2011.
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Others

  • X. Li, W. Wiedermann. On the Causal Relation of Academic Achievement and Intrinsic Motivation. Direction Dependence in Statistical Modeling: Methods of Analysis, pp. xx-xx, 2020.
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  • T. Rosenström, R. García-Velázquez. Distribution-Based Causal Inference. Direction Dependence in Statistical Modeling: Methods of Analysis, pp. xx-xx, 2020.
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  • L. Bickman. Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health. Administration and Policy in Mental Health and Mental Health Services Research, pp. xx-xx, 2020.
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  • Q. Bi, K. E. Goodman, J. Kaminsky, J. Lessler. What Is Machine Learning: a Primer for the Epidemiologist. American Journal of Epidemiology, pp. xx-xx, 2019.
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  • J. Runge, S. Bathiany, E. Bollt, G. Camps-Valls, D. Coumou, E. Deyle, C. Glymour, M. Kretschmer, M. D. Mahecha, J. Muñoz-Marí, E. H. van Nes, J. Peters, R. Quax, M. Reichstein, M. Scheffer, B. Schölkopf, P. Spirtes, G. Sugihara, J. Sun, K. Zhang, and J. Zscheischler . Inferring causation from time series in Earth system sciences. Nature Communications, 10, Article number: 2553, 2019.
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  • D. Malinsky and D. Danks. Causal discovery algorithms: A practical guide. Philosophy Compass, pp. xx-xx, 2017.
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  • G. Camps-Valls, J. Muñoz-Marí, J. Verrelst, F. Mateo, and J. Gomez-Dans. A survey on Gaussian processes for earth-observation data analysis: a comprehensive investigation. IEEE Geoscience and Remote Sensing Magazine, pp. xx-xx, 2016.
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  • L. A. Cox and J. E. Goodman. Re:“Estimating causal associations of fine particles with daily deaths in Boston”. American Journal of Epidemiology, xx(xx-xx): xx--xx, 2016. In press.
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  • A. von Eye and W. Wiedermann. Fellow scholars: Let’s liberate ourselves from scientific machinery. Research in Human Development, 12(3-4): 246--254, 2015.
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  • E. Brunk and U. Rothlisberger. Mixed quantum mechanical/molecular mechanical molecular dynamics simulations of biological systems in ground and electronically excited states. Chemical Reviews, xx: xx--xx, 2015.
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  • A. Hannart, J. Pearl, F. Otto, P. Naveau, and M. Ghil. Causal counterfactual theory for the attribution of weather and climate-related events. Bulletin of the American Meteorological Society, xx: xx--xx, 2015. In press.
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  • K. Chalupka, P. Perona, and F. Eberhardt. Visual causal feature learning. Arxiv preprint arXiv:1412.2309, 2014. Accepted at UAI2015.
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  • L. Bottou, J. Peters, J. Quiñonero-Candela, D. X. Charles, D. M. Chickering, E. Portugaly, D. Ray, P. Simard and E. Snelson. Counterfactual reasoning and learning systems: The example of computational advertising. Journal of Machine Learning Research, 14: 3207--3260, 2013.
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