You're reading from Causal Inference and Discovery in Python

Product typeBook

Published inMay 2023

PublisherPackt

ISBN-139781804612989

Edition1st Edition

Concepts

Data Science

Author (1)

Aleksander Molak

References

Becker, T. E., Atinc, G., Breaugh, J. A., Carlson, K. D., Edwards, J. R., & Spector, P. E. (2016). Statistical control in correlational studies: 10 essential recommendations for organizational researchers. Journal of Organizational Behavior, 37(2), 157–167.

Bollen, K. A. & Noble, M. D. (2011). Structural equation models and the quantification of behavior. PNAS Proceedings of the National Academy of Sciences of the United States of America, 108(Suppl 3), 15639–15646.

Cinelli, C., Forney, A., & Pearl, J. (2022). A Crash Course in Good and Bad Controls. Sociological Methods & Research, 0 (0), 1-34.

Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling. Guilford Press.

Murphy, K. P. (2022). Probabilistic Machine Learning: An Introduction. MIT Press.

Pearl, J. (2012). The causal foundations of structural equation modeling. In Hoyle, R. H. (Ed.), Handbook of structural equation modeling (pp. 68–91). Guilford...

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Causal Inference and Discovery in Python

Published in: May 2023Publisher: PacktISBN-13: 9781804612989

Author (1)

Aleksander Molak

Aleksander Molak is a Machine Learning Researcher and Consultant who gained experience working with Fortune 100, Fortune 500, and Inc. 5000 companies across Europe, the USA, and Israel, designing and building large-scale machine learning systems. On a mission to democratize causality for businesses and machine learning practitioners, Aleksander is a prolific writer, creator, and international speaker. As a co-founder of Lespire, an innovative provider of AI and machine learning training for corporate teams, Aleksander is committed to empowering businesses to harness the full potential of cutting-edge technologies that allow them to stay ahead of the curve.
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