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Theory
C. S. Ding. Bayesian Network for Discovering the Potential Causal Structure in Observational Data. In Dependent Data in Social Sciecnes Research - Forms, Issues and Methods of Analysis (2nd edition), pages 259–286. Springer, 2024.
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[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.
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[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").
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[pdf] [Google scholar]A. Hyvärinen. Independent component analysis: Recent advances. Philosophical Transactions of the Royal Society A, 371: 20110534, 2013.
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[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.
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Neuroscience
V. Pichot, C. Corbier, F. Chouchou. The contribution of granger causality analysis to our understanding of cardiovascular homeostasis: from cardiovascular and respiratory interactions to central autonomic network control. Frontiers in Network Physiology, pp. xx--xx, 2024.
[pdf] [Google scholar]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.
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[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.
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[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.
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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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[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)
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[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.
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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.
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Others
B. Youngmann, M. Cafarella, A. Gilad, S. Roy. Summarized Causal Explanations For Aggregate Views. Proceedings of the ACM on Management of Data, 2(1): 71, 2024.
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[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.
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[pdf] [Google scholar]L. A. Cox. Toward practical causal epidemiology. Global Epidemiology, pp. xx-xx, 2021.
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[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.
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[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.
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