DATA
“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.”
—Sean J. Taylor
Chief Scientist at Motif Analytics
“Causal Inference in
Python 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
Causal Inference in Python
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How many buyers will an additional dollar of online marketing
attract? Which customers will only buy when given a discount
coupon? How do you establish an optimal pricing strategy?
Causal inference is the best way to determine how the levers
at your disposal affect the business metrics you want to drive.
And it only requires a few lines of Python code.
In this book, author Matheus Facure explains the largely
untapped potential of causal inference for estimating impacts
and eects. Managers, data scientists, and business analysts
will learn classical causal inference methods, like A/B tests,
linear regression, propensity score, synthetic controls, and
dierence-in-dierences—and modern developments such as
using machine learning for heterogeneous effect estimation.
Each method is illustrated by an application in the industry.
This book helps you:
• Learn how to use basic concepts of causal inference
• Frame a business problem as a causal inference problem
• Understand how bias interferes with causal inference
• Learn how causal eects can dier from person to person
• Use observations of the same customers across time for
causal inference
• Use geo and switchback experiments when randomization
isn’t an option
• Examine noncompliance bias and eect dilution
Matheus Facure is an economist and
senior data scientist at Nubank, the
biggest FinTech company outside Asia.
He’s successfully applied causal
inference in a wide range of business
scenarios, from automated and real-time
interest and credit decision-making,
to cross-selling emails and optimizing
marketing budgets. He’s the author of
Causal Inference for the Brave and True.
9 781098 140250
57999
US $79.99 CAN $99.99
ISBN: 9781098140250
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
Causal inference tools play a major role in guiding decision-making. In this engaging
book, Matheus Facure provides a clear introduction to these tools, paying particular
attention to how to use them in practice. The business applications and detailed
Python code will help you get the job done.
—Pedro H. C. Sant’Anna, Emory University
and Causal Solutions
Matheus Facure
Causal Inference in Python
Applying Causal Inference in the Tech Industry
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