The scientific method aims at the discovery and modeling of causal relationships from data. It is not enough to know that smoking and cancer are correlated; the important thing is to know that if we start smoking or stop smoking, it will change our chance of getting cancer. Artificial Intelligence and Machine learning as it exists today does not take causation into account and instead make predictions based on statistical associations. This can give rise to problems when they are used in environments in which the associations used are not necessarily fulfilled or when such models are used for decision making. This picture has begun to change with recent advances in techniques for causal inference, which make it possible (under certain circumstances) to measure causal relationships from observational and experimental data and, in general, to make formal reasoning about cause and effect. As we will discuss in the talk, the convergence between machine learning and causal inference opens the door to answering questions relevant to many AI tasks.
Software:
Bibliography:
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Basic:
- Pearl, Judea, and Dana Mackenzie. The book of why: the new science of cause and effect. Basic books, 2018.
- Neal, Brady. "Introduction to causal inference from a machine learning perspective." Course Lecture Notes (draft) (2020).
- Imbens, G. W., & Rubin, D. B. (2015). Causal inference in statistics, social, and biomedical sciences. Cambridge University Press.
- Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.
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Advanced:
- Glymour, Madelyn, Judea Pearl, and Nicholas P. Jewell. Causal inference in statistics: A primer. John Wiley & Sons, 2016. Press.
- Hünermund, Paul, and Elias Bareinboim. "Causal inference and data fusion in econometrics." The Econometrics Journal, forthcoming. Also in arXiv preprint arXiv:1912.09104 (2019).
- Peters, Jonas, Dominik Janzing, and Bernhard Schölkopf. Elements of causal inference: foundations and learning algorithms. The MIT Press, 2017.
- Kaddour, J., Lynch, A., Liu, Q., Kusner, M. J., & Silva, R. (2022). Causal machine learning: A survey and open problems. arXiv preprint arXiv:2206.15475.
- Mitchell, Shira, et al. "Algorithmic fairness: Choices, assumptions, and definitions." Annual Review of Statistics and Its Application 8 (2021): 141-163.
- Schölkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., & Bengio, Y. (2021). Toward causal representation learning. Proceedings of the IEEE, 109(5), 612-634.
- Schölkopf, B. (2022). Causality for machine learning. In Probabilistic and Causal Inference: The Works of Judea Pearl (pp. 765-804).
- Hünermund, Paul and Kaminski, Jermain and Schmitt, Carla, Causal Machine Learning and Business Decision Making (February 19, 2022). Available at SSRN: https://ssrn.com/abstract=3867326 or http://dx.doi.org/10.2139/ssrn.3867326.