Book description
How many buyers will an additional dollar of online marketing bring in? Which customers will only buy when given a discount coupon? How do you establish an optimal pricing strategy? The best way to determine how the levers at our disposal affect the business metrics we want to drive is through causal inference.
In this book, author Matheus Facure, senior data scientist at Nubank, explains the largely untapped potential of causal inference for estimating impacts and effects. Managers, data scientists, and business analysts will learn classical causal inference methods like randomized control trials (A/B tests), linear regression, propensity score, synthetic controls, and differenceindifferences. Each method is accompanied by an application in the industry to serve as a grounding example.
With this book, you will:
 Learn how to use basic concepts of causal inference
 Frame a business problem as a causal inference problem
 Understand how bias gets in the way of causal inference
 Learn how causal effects can differ from person to person
 Use repeated observations of the same customers across time to adjust for biases
 Understand how causal effects differ across geographic locations
 Examine noncompliance bias and effect dilution
Publisher resources
Table of contents
 Preface
 I. Fundamentals
 1. Introduction to Causal Inference
 2. Randomized Experiments and Stats Review
 3. Graphical Causal Models
 II. Adjusting for Bias

4. The Unreasonable Effectiveness of Linear Regression
 All You Need Is Linear Regression
 Regression Theory
 FrischWaughLovell Theorem and Orthogonalization
 Regression as an Outcome Model
 Positivity and Extrapolation
 Nonlinearities in Linear Regression
 Regression for Dummies
 Omitted Variable Bias: Confounding Through the Lens of Regression
 Neutral Controls
 Key Ideas
 5. Propensity Score
 III. Effect Heterogeneity and Personalization
 6. Effect Heterogeneity
 7. Metalearners
 IV. Panel Data
 8. DifferenceinDifferences
 9. Synthetic Control
 V. Alternative Experimental Designs
 10. Geo and Switchback Experiments
 11. Noncompliance and Instruments
 12. Next Steps
 Index
 About the Author
Product information
 Title: Causal Inference in Python
 Author(s):
 Release date: July 2023
 Publisher(s): O'Reilly Media, Inc.
 ISBN: 9781098140250
You might also like
book
Causal Inference and Discovery in Python
Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine …
book
Python for Data Analysis, 3rd Edition
Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python …
book
Fluent Python, 2nd Edition
Don't waste time bending Python to fit patterns you've learned in other languages. Python's simplicity lets …
book
Python Data Science Handbook, 2nd Edition
Python is a firstclass tool for many researchers, primarily because of its libraries for storing, manipulating, …