You're reading from Causal Inference and Discovery in Python

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Published inMay 2023

PublisherPackt

ISBN-139781804612989

Edition1st Edition

Concepts

Data Science

Author (1)

Aleksander Molak

References

Barabási, A. L. (2009). Scale-free networks: a decade and beyond. Science, 325(5939), 412-413.

Barabási, A. L., and Albert, R. (1999). Emergence of Scaling in Random Networks. Science, 286(5439), 509–512.

Blake, W. (2009) The Tyger. Songs of Experience. In William Blake: The Complete Illuminated Books.

Cai, R., Wu, S., Qiao, J., Hao, Z., Zhang, K., and Zhang, X. (2021). THP: Topological Hawkes Processes for Learning Granger Causality on Event Sequences. ArXiv.

Chickering, D. M. (2003). Optimal structure identification with greedy search. J. Mach. Learn. Res., 3, 507–554.

Chickering, M. (2020). Statistically Efficient Greedy Equivalence Search. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI). In Proceedings of Machine Learning Research, 124, 241-249.

Colombo, D., and Maathuis, M.H. (2012). Order-independent constraint-based causal structure learning. J. Mach. Learn. Res., 15, 3741-3782.

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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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