BIBFRAME Work

Title

Causal inference for data science

Type

• Text
• Monograph

Contribution

Villa Robert, Aleix Ruiz de (author )

Subject

• Variables instrumentales (Statistique) (RVM)
• Inférence (Logique) (RVM)
• Quantitative research (LCSH)
• Data structures (Computer science) (LCSH)
• Instrumental variables (Statistics) (LCSH)
• Machine learning (LCSH)
• Recherche quantitative (RVM)
• Inference (LCSH)
• Structures de données (Informatique) (RVM)
• Apprentissage automatique (RVM)
• Causation (LCSH)

Language

• English

Illustrative Content

• illustrations

Classification

• DDC: 006.31 (Assigner: dlc)
• LCC: Q325.5 .V55 2025 (Assigner: dlc)

Content

• text

Supplementary content

• index

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.

Table of 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.

Authorized Access Point

Villa Robert, Aleix Ruiz de. Causal inference for data science

Admin Metadata

• Date: 2026-09-11T20:15:09.884759+00:00
• Agent: cbc
• Status: c
• Derived from: 24007365

Admin Metadata

• Date: 2026-09-11
• Cataloger id: Unknown
• Description level: http://id.loc.gov/ontologies/bibframe-2-6-0/
• Description language: eng
• Description authentication: pcc