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Related reviews and papers
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]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.
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[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.
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[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)
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[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.
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[pdf] [Google scholar]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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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.
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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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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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