Causal Inference for Data Science.

Format:

Book

Author/Creator:

Ruiz de Villa Robert, Aleix.

Language:

English

Subjects (All):

Machine learning.

Quantitative research.

Data structures (Computer science).

Causation.

Inference.

Instrumental variables (Statistics).

Physical Description:

1 online resource (316 pages)

Edition:

1st ed.

Place of Publication:

New York : Manning Publications Co. LLC, 2025.

Summary:

Why did you get a particular result? What would have lead to a different outcome? These are the essential questions of causal inference. This powerful methodology improves your decisions by connecting cause and effect-- even when you can't run experiments, A/B tests, or expensive controlled trials. Causal inference for data science introduces techniques to apply causal reasoning to ordinary business scenarios. And with this clearly-written, practical guide, you won't need advanced statistics or high-level math to put causal inference into practice! By applying a simple approach based on Directed Acyclic Graphs (DAGs), you'll learn to assess advertising performance, pick productive health treatments, deliver effective product pricing, and more.

Contents:

Part 1. Interence and the role of confounders. Introducing causality

First steps: Working with confounders

Applying causal inference

How machine learning and causal inference can help each other

Part 2. The adjustment formula in practice. Finding comparable cases with propensity scores

Direct and indirect effects with linear models

Dealing with complex graphs

Advanced tools with the DoubleML library

Part 3. Other strategies beyond the adjustment formula. Instrumental variables

Potential outcomes framework

The effect of a time-related event.

Notes:

Description based on publisher supplied metadata and other sources.

Includes index.

ISBN:

9781638356462

1638356467

OCLC:

1496388563