Causal Inference in Python | Applying Causal Inference in the Tech Industry

Matheus Facure

ISBN 10: 1098140257 / ISBN 13: 9781098140250

Published by O'Reilly Media, 2023

About this Item

Causal Inference in Python | Applying Causal Inference in the Tech Industry | Matheus Facure | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2023 | O'Reilly Media | EAN 9781098140250 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

Seller Inventory # 126899258

Bibliographic Details

• Title: Causal Inference in Python | Applying Causal Inference in the Tech Industry
• Publisher: O'Reilly Media
• Publication Date: 2023
• Language: English
• ISBN 10: 1098140257
• ISBN 13: 9781098140250
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 227W x 192H x 23D millimeters 227 millimeters width by 192 millimeters height by 23 millimeters depth
• Weight: 698 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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 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

About the Author

Matheus Facure is an Economist and Senior Data Scientist at Nubank, the biggest FinTech company outside Asia. His has successfully applied causal inference in a wide range of business scenarios, from automated and real time interest and credit decision making, to cross sell emails and optimizing marketing budgets. He is also author of Causal Inference for the Brave and True, a popular book which aims at making causal inference mainstream in a light-hearted, yet rigorous way.