Related reviews
Related reviews and papers
Theory
NEW L. Wang, S. Huang, S. Wang, J. Liao, T. Li, L. Liu. A survey of causal discovery based on functional causal model. Engineering Applications of Artificial Intelligence, Volume 133, Part C, 108258, 2024.
[pdf] [Google scholar]NEW V. Chavez-Demoulin, L. Mhalla. Causality and extremes. arXiv:2403.05331, 2024.
[pdf] [Google scholar]NEW E. Kummerfeld, B. Andrews, S. Ma. Foundations of Causal ML. In: Simon, G.J., Aliferis, C. (eds) Artificial Intelligence and Machine Learning in Health Care and Medical Sciences. Health Informatics, Springer, Cham, 2024.
[pdf] [Google scholar]H. Chen, K. Du, C. Li, X. Yang. A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representations. arXiv:2311.00923, 2023.
[pdf] [Google scholar]J. Runge, A. Gerhardus, G. Varando, V. Eyring, and G. Camps-Valls. Causal inference for time series. Nature Reviews Earth & Environment, 4, 487–505, 2023.
[pdf] [Google scholar]G. Camps-Valls, A. Gerhardus, U. Ninad, G. Varando, G. Martius, E. Balaguer-Ballester, R. Vinuesa, E. Diaz, L. Zanna, Jakob Runge. Discovering causal relations and equations from data. Arxiv preprint arXiv:2305.13341, 2023.
[pdf] [Google scholar]U. Hasan, E. Hossain, M. O. Gani. A Survey on Causal Discovery Methods for Temporal and Non-Temporal Data. Arxiv preprint arXiv:2303.15027, 2023.
[pdf] [Google scholar]孙悦雯， 柳文章， 孙长银. 基于因果建模的强化学习控制: 现状及展望. 自动化学报, 49(3): 661-677. 2023.
[pdf] [Google scholar]C. Gong, D. Yao, C. Zhang, W. Li, J. Bi. Causal Discovery from Temporal Data: An Overview and New Perspectives. Arxiv preprint arXiv:2303.10112, 2023.
[pdf] [Google scholar]Wai-yin Lam. Causal Razors. Arxiv preprint arXiv:2302.02672, 2023.
[pdf] [Google scholar]A. Hyvärinen, I. Khemakhem, R. Monti. Identifiability of latent-variable and structural-equation models: from linear to nonlinear. Arxiv preprint arXiv:2302.02672, 2023.
[pdf] [Google scholar]P. Upadhyaya, K. Zhang, C. Li, X. Jiang, Y. Kim. Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine. JMIR Medical Informatics, 11, 2023.
[pdf] [Google scholar]M. C. Vonk, N. Malekovic, A. Kononova, T. Bäck. Disentangling causality: assumptions in causal discovery and inference. Research Squre, 2022.
[pdf] [Google scholar]J. Berrevoets, K. Kacprzyk, Z. Qian, M. van der Schaar. Navigating causal deep learning. Arxiv preprint arXiv:2212.00911, 2022.
[pdf] [Google scholar]W. Zhou and Q. C. Chen. A survey on causal discovery. Knowledge Graph and Semantic Computing: Knowledge Graph Empowers the Digital Economy (CCKS2022), 2022.
[pdf] [Google scholar]A. Rawal, A. Raglin, D. B. Rawat, B. M. Sadler. Causality and Machine Learning Review. ARL-TR-9599, 2022.
[pdf] [Google scholar]H. Chen, K. Du, X. Yang, C. Li . A Review and Roadmap of Deep Learning Causal Discovery in Different Variable Paradigms. Arxiv preprint arXiv:2209.06367, 2022.
[pdf] [Google scholar]A. Zanga, E. Ozkirimli, F. Stella. A survey on causal discovery: Theory and practice. International Journal of Approximate Reasoning, xx(xx), 2022
[pdf] [Google scholar]S. Shimizu. Statistical causal discovery: LiNGAM approach. SpringerBriefs in Statistics. Springer Tokyo, 2022.
[pdf] [draft] [Google scholar]K. Zhang. Computational causal discovery: Advantages and assumptions. THEORIA, 37(1), 2022.
[pdf] [Google scholar]C. K. Assaad, E. Devijver, E. Gaussier. Survey and Evaluation of Causal Discovery Methods for Time Series. Journal of Artificial Intelligence Research, 73: xx-xx, pp. xx-xx, 2022.
[pdf] [Google scholar]W. Wiedermann. Third moment-based causal inference. Behaviormetrika, 20(2): xx-xx, pp. xx-xx, 2022.
[pdf] [Google scholar]P. Upadhyaya, K. Zhang, C. Li, X. Jiang, Y. Kim. Scalable Causal Structure Learning: New Opportunities in Biomedicine. Arxiv preprint arXiv:2110.07785, 2021.
[pdf] [Google scholar]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.
[pdf] [Google scholar]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.
[pdf] [Google scholar]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.
[pdf] [Google scholar]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.
[pdf] [Google scholar]Y. Luo, J. Peng, and J. Ma. When causal inference meets deep learning. Nature Machine Intelligence volume, 2, pp. 426-427, 2020.
[pdf] [Google scholar]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.
[pdf] [Google scholar]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.
[pdf] [Google scholar]F. Eberhardt. Beyond Cause-Effect Pairs. Cause Effect Pairs in Machine Learning, pp. 215-233, 2019.
[pdf] [Google scholar]D. Janzing. The Cause-Effect Problem: Motivation, Ideas, and Popular Misconceptions. Cause Effect Pairs in Machine Learning, pp. 3-26, 2019.
[pdf] [Google scholar]I. Guyon, O. Goudet, D. Kalainathan. Evaluation Methods of Cause-Effect Pairs. Cause Effect Pairs in Machine Learning, pp. 27-99, 2019.
[pdf] [Google scholar]O. Goudet, D. Kalainathan, M. Sebag, I.Guyon. Learning Bivariate Functional Causal Models. Cause Effect Pairs in Machine Learning, pp. 101-153, 2019.
[pdf] [Google scholar]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.
[pdf] [Google scholar]C. Glymour, K. Zhang, and P. Spirtes. Review of Causal Discovery Methods Based on Graphical Models. Frontiers in Genetics, xx(xx): xx-xx, 2019.
[pdf] [Google scholar]N. Climenhaga, L. DesAutels, G. Ramsey. Causal Inference from Noise. Noûs, xx(xx): xx-xx, 2019.
[Google scholar]J. Pearl. The seven tools of causal inference with reflections on machine learning. Communications of the ACM, 62(3), 54-60, 2019.
[pdf] [Google scholar]B. Schölkopf. CAUSALITY FOR MACHINE LEARNING. Arxiv preprint arXiv:1911.10500, 2019.
[pdf] [Google schlar]J. Runge. Causal network reconstruction from time series: From theoretical assumptions to practical estimation. Chaos, 28: 075310, 2018.
[pdf] [Google scholar]S. Shimizu. Non-Gaussian methods for causal structure learning. Prevention Science, xx--xx, 2018.
[pdf] [Google scholar]C. Heinze-Deml, M. H. Maathuis, and N. Meinshausen. Causal structure learning. Arxiv preprint arXiv:1706.09141, 2017.
[pdf] [Google scholar]J. Peters, D. Janzing, and B. Schölkopf. Elements of causal inference: foundations and learning algorithms. MIT Press, 2017.
[Google scholar]K. Zhang, B. Schölkopf, P. Spirtes, and C. Glymour. Learning causality and causality-related learning: Some recent progress. National Science Review, nwx137, 2017.
[pdf] [Google scholar]D. Janzing. Statistical asymmetries between cause and effect. Time in Physics, 129--139, 2017.
[pdf] [Google scholar]M. Drton and M. H. Maathuis. Structure learning in graphical modeling. Arxiv preprint arXiv:1606.02359, 2016.
[pdf] [Google scholar]F. Eberhardt. Introduction to the foundations of causal discovery. International Journal of Data Science and Analytics, xx(xx): xx--xx, 2016.
[pdf] [Google scholar]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)
[pdf] [Google scholar]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.
[pdf] [Google scholar]S. Shimizu. Non-Gaussian structural equation models for causal discovery. Statistics and Causality: Methods for Applied Empirical Research, pp. 153-184, 2016.
[pdf] [Google scholar]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.
[pdf] [Google scholar]P. Spirtes and K. Zhang. Causal discovery and inference: concepts and recent methodological advances. Applied Informatics, xx(xx): xx--xx, 2016.
[pdf] [Google scholar]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.
[pdf] [Google scholar]M. H. Maathuis and P. Nandy. A review of some recent advances in causal inference. Handbook of Big Data, pp. xx--xx, 2015.
[pdf] [Google scholar]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).
[pdf] [Google scholar]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).
[pdf] [Google scholar]S. Shimizu. LiNGAM: Non-Gaussian methods for estimating causal structures. Behaviormetrika, 41(1): 65--98, 2014 (Special Issue on Causal Discovery).
[pdf] [Google scholar]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").
[pdf] [Google scholar]K. Bollen and J. Pearl. Eight myths about causality and structural equation models. Handbook of Causal Analysis for Social Research, pp. 301-328, 2013.
[pdf] [Google scholar]A. Hyvärinen. Independent component analysis: Recent advances. Philosophical Transactions of the Royal Society A, 371: 20110534, 2013.
[pdf] [Google scholar]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.
[doc] [Google scholar]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.
[pdf] [Google scholar]J. Pearl. Causal inference in statistics: An overview. Statistics Surveys 3: 96--146, 2009.
[pdf] [Google scholar]J. Pearl. Causality: models, reasoning and inference. Cambridge University Press, 2000. (2nd eds. 2009).
[Book webpage] [Google scholar]D. B. Rubin. Causal inference using potential outcomes. Journal of the American Statistical Association 100(469): 322--331, 2005.
[pdf] [Google scholar]M. A. Hernán. A definition of causal effect for epidemiological research. Journal of Epidemiology & Community Health 58: 265--271, 2004.
[pdf] [Google scholar]P. W. Holland. Statistics and causal inference. Journal of the American Statistical Association 81(396): 945--960, 1986.
[pdf] [Google scholar]
Neuroscience
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.
[pdf] [Google scholar]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.
[pdf] [Google scholar]C. Glymour and R. Sanchez-Romero. Underdetermination, behavior and the brain. In The Routledge Handbook of the Computational Mind, pp. xx-xx, 2019. Routledge.
[pdf] [Google scholar]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.
[pdf] [Google scholar]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)
[pdf] [Google scholar]C. Glymour and C. Hanson. Reverse inference in neuropsychology. British Journal for the Philosophy of Science, pp. xx--xx, 2015.
[pdf] [Google scholar]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.
[pdf] [Google scholar]J. A. Mumford and J. D. Ramsey. Bayesian networks for fMRI: A primer. NeuroImage 86(1): 573--582, 2014.
[pdf] [Google scholar]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.
[pdf] [The human connectome project webpage] [Google scholar]S. M. Smith. The future of FMRI connectivity. NeuroImage 62(2): 1257--1266, 2012.
[pdf] [Google scholar]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.
[pdf] [Google scholar]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.
[pdf] [Google scholar]
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.
[pdf] [Google scholar]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.
[pdf] [Google scholar]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)
[pdf] [Google scholar]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.
[pdf] [Google scholar]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.
[pdf] [real data: GSE27869 and GSE27870] [Google scholar]P. Bühlmann. Causal statistical inference in high dimensions. Mathematical Methods of Operations Research, 77(3): 357-370, 2013.
[pdf] [Google scholar]D. Pe'er and N. Hacohen. Principles and strategies for developing network models in cancer. Cell, 144(6): 864-873, 2011.
[pdf] [Google scholar]
Economics/Finance/Marketing
X. Dong, H. Dai, Y. Fan, S. Jin, S. Rajendran, K. Zhang. On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factors. arXiv:2401.05414, 2024.
[pdf] [Google scholar]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.
[pdf] [Google scholar]
Others
L. I. Yuan, M. A. Xinyu, Y. Guoli, Z. Huiqun, S. Wei. Survey of Causal Inference for Knowledge Graphs and Large Language Models. Journal of Frontiers of Computer Science & Technology,17(10): 2358-2376, 2023.
[pdf] [Google scholar]J. Su, D. Chen, D. Zheng, Y. Su, X. Li . The insight of why: Causal inference in Earth system science. Science China Earth Sciences, xx(xx), pp. xx-xx, 2023.
[pdf] [Google scholar]D. Fujiwara, K. Koyama, K. Kiritoshi, T. Okawachi, T. Izumitani, and S. Shimizu. Prospects of Continual Causality for Industrial Applications. In Proc. First AAAI Bridge Program on Continual Causality, PMLR 208:18-24, 2023.
[pdf] [Google scholar]T. H. Rosenström,N. O. Czajkowski, O. A. Solbakken, S. E. Saarni. Direction of dependence analysis for pre-post assessments using non-Gaussian methods: a tutorial. Psychotherapy Research, xx(xx), pp. xx-xx, 2023.
[pdf] [Google scholar]S. Xu, J. Ji, Y. Li, Y. Ge, J. Tan, Y. Zhang . Causal Inference for Recommendation: Foundations, Methods and Applications. Arxiv preprint arXiv:2301.04016, 2023.
[pdf] [Google scholar]Y. Li. Deep Causal Learning for Robotic Intelligence. Arxiv preprint arXiv: 2212.1259, 2022.
[pdf] [Google scholar]J. Corander, W. P. Hanage, J. Pensar. Causal discovery for the microbiome. The Lancet Microbe, xx(xx):xx, 2022.
[pdf] [Google scholar]R. Binkytė-Sadauskienė, K. Makhlouf, C. Pinzón, S. Zhioua, C. Palamidessi. Causal Discovery for Fairness. Arxiv preprint arXiv:2206.06685 , 2022.
[pdf] [Google scholar]M. Vuković, S. Thalmann. Causal Discovery in Manufacturing: A Structured Literature Review. Journal of Manufacturing and Materials Processing, 6(1):10, 2022.
[pdf] [Google scholar]J. Wang, J. Zhou, X. Chen. Probabilistic Graphical Model for Continuous Variables. Intelligent Control and Learning Systems, vol 3, pp. 251-265, 2022.
[pdf] [Google scholar]J. Wang, J. Zhou, X. Chen. Bayesian Causal Network for Discrete Variables. Intelligent Control and Learning Systems, vol 3, pp. 233-239, 2022.
[pdf] [Google scholar]L. A. Cox. Toward practical causal epidemiology. Global Epidemiology, pp. xx-xx, 2021.
[pdf] [Google scholar]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.
[pdf] [Google scholar]T. Rosenström, R. García-Velázquez. Distribution-Based Causal Inference. Direction Dependence in Statistical Modeling: Methods of Analysis, pp. xx-xx, 2020.
[pdf] [Google scholar]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.
[pdf] [Google scholar]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.
[pdf] [Google scholar]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.
[pdf] [Google scholar]D. Malinsky and D. Danks. Causal discovery algorithms: A practical guide. Philosophy Compass, pp. xx-xx, 2017.
[pdf] [Google scholar]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.
[pdf] [Google scholar]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.
[pdf] [Google scholar]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.
[pdf] [Google scholar]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.
[pdf] [Google scholar]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.
[pdf] [Google scholar]K. Chalupka, P. Perona, and F. Eberhardt. Visual causal feature learning. Arxiv preprint arXiv:1412.2309, 2014. Accepted at UAI2015.
[pdf] [Google scholar]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.
[pdf] [Google scholar]