Principles of Causal Inference
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Books
Recommended Books
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Pearl, J., Glymour, M. and Jewell, N.P., 2016. Causal inference in statistics: A primer. John Wiley & Sons.
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Hernán, M.A. and Robins, J.M., 2020. Causal inference: what if. Boca Raton: Chapman & Hill/CRC, 2020.
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Neal, Brady. 2020. Introduction to Causal Inference from a Machine Learning Perspective Unpublished Draft.
Additional References
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Pearl, J. and Mackenzie, D. (2018). The book of Why. The new science of cause and effect. Basic Books.
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Cunningham, D. (2021) Causal Inference. The Mixtape. Yale University Press.
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Huntington-Klein, N. (2021). The Effect: An Introduction to Research Design and Causality. CRC Press.
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Pearl, J., 2009. Causality. Cambridge university press.
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Rosenbaum, P.R., 2017. Observation and experiment. Harvard University Press.
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Imbens, G.W. and Rubin, D.B., 2015. Causal inference in statistics, social, and biomedical sciences. Cambridge University Press.
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Morgan, S.L. and Winship, C., 2015. Counterfactuals and causal inference. Cambridge University Press.
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Spirtes, P., Glymour, C.N., Scheines, R. and Heckerman, D., 2000. Causation, prediction, and search. MIT press.
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Berzuini, C., Dawid, P. and Bernardinell, L. eds., 2012. Causality: Statistical perspectives and applications. John Wiley & Sons.
- Brumback, B. (2022). Fundamentals of Causal Inference. CRC Press
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