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Causal Inference in Python : Applying Causal Inference in the Tech Industry by Matheus Facure (2023, Trade Paperback)
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About this product
Product Identifiers
PublisherO'reilly Media, Incorporated
ISBN-101098140257
ISBN-139781098140250
eBay Product ID (ePID)6060637961
Product Key Features
Number of Pages406 Pages
Publication NameCausal Inference in Python : Applying Causal Inference in the Tech Industry
LanguageEnglish
Publication Year2023
SubjectData Modeling & Design, Skills, Statistics, Programming Languages / Python
TypeTextbook
AuthorMatheus Facure
Subject AreaComputers, Business & Economics
FormatTrade Paperback
Dimensions
Item Height1 in
Item Weight25.1 Oz
Item Length9.2 in
Item Width7 in
Additional Product Features
LCCN2023-282031
Dewey Edition23
IllustratedYes
Dewey Decimal005.13/3
SynopsisHow 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 difference-in-differences. 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, 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 effects. Managers, data scientists, and business analysts will learn classical causal inference methods, like A/B tests, linear regression, propensity score, synthetic controls, and difference-in-differences-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 effects can differ 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 effect dilution
LC Classification NumberQA76.73.P98F33 2023
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