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

Автор: Facure Matheus

Дата выхода: 2023

Издательство: O’Reilly Media, Inc.

Количество страниц: 540

Размер файла: 4.2 MB

Тип файла: PDF

Добавил: codelibs

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Introduction to Rust for Statisticians
Data Handling and Preprocessing
Descriptive Statistics in Rust
Probability Distributions and Random Variables
Inferential Statistics
Regression Analysis
Bayesian Statistics
Multivariate Statistical Methods
Nonlinear Models and Machine Learning
Model Evaluation and Validation
Text and Natural Language Processing

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

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