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

Athey, S., Tibshirani, J., & Wager, S. (2018). Generalized random forests. The Annals of Statistics, 47(2). 1148-1178.

Balestriero, R., Pesenti, J., & LeCun, Y. (2021). Learning in High Dimension Always Amounts to Extrapolation. arXiv, abs/2110.09485.

Barr, D. J. (2008). Analyzing “visual world” eyetracking data using multilevel logistic regression. J. Mem. Lang. 59, 457-474.

Baayen, R. H., Davidson, D. J., and Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. J. Mem. Lang. 59, 390-412.

Bühlmann, P. & van de Geer, S. A. (2011). Statistics for High-Dimensional Data. Springer.

Cassel, C.M., Särndal, C., & Wretman, J. H. (1976). Some results on generalized difference estimation and generalized regression estimation for finite populations. Biometrika, 63, 615-620.

Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. M. (2018)....

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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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