Praise for Causal Inference in Python
Causal inference is one of the most important approaches
for modern data scientists, but there’s still a big gap between
theory and applications. Matheus has written the best book
yet to teach you how to go from toy models to state-of-the-
art methods that work on real data and solve important,
practical problems. I’m excited to finally have the perfect
resource to recommend that clearly explains the latest
approaches and provides detailed code and examples for
those who learn by doing.
—Sean J. Taylor, Chief Scientist at Motif
Analytics
The analyst who avoids answering all causal questions is
limiting themselves greatly, and the analyst who answers
them carelessly is asking for trouble. Facure’s book is an
accessible introduction to causal inference, focusing on the
tools and contexts most familiar to the Python data
analytics community
—Nick Huntington-Klein, Professor of
Economics and author of The Effect: An
Introduction to Research Design and Causality
