Causal Inference for Data Science

Alex Ruiz de Villa

ISBN 10: 1633439658 / ISBN 13: 9781633439658

Editore: Manning Publications Jan 2025, 2025

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Neuware - When you know the cause of an event, you can affect its outcome. This accessible introduction to causal inference shows you how to determine causality and estimate effects using statistics and machine learning.A/B tests or randomized controlled trials are expensive and often unfeasible in a business environment. Causal Inference for Data Science reveals the techniques and methodologies you can use to identify causes from data, even when no experiment or test has been performed. In Causal Inference for Data Science you will learn how to: Model reality using causal graphs Estimate causal effects using statistical and machine learning techniques Determine when to use A/B tests, causal inference, and machine learning Explain and assess objectives, assumptions, risks, and limitations Determine if you have enough variables for your analysis It s possible to predict events without knowing what causes them. Understanding causality allows you both to make data-driven predictions and also intervene to affect the outcomes. Causal Inference for Data Science shows you how to build data science tools that can identify the root cause of trends and events. You ll learn how to interpret historical data, understand customer behaviors, and empower management to apply optimal decisions. Purchase of the print book includes a free Elektronisches Buch in PDF and ePub formats from Manning Publications. About the technology 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. About the book 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. What's inside When to use A/B tests, causal inference, and ML Assess objectives, assumptions, risks, and limitations Apply causal inference to real business data About the reader For data scientists, ML engineers, and statisticians. About the author Aleix Ruiz de Villa Robert is a data scientist with a PhD in mathematical analysis from the Universitat Autònoma de Barcelona. Table of Contents Part 1 1 Introducing causality 2 First steps: Working with confounders 3 Applying causal inference 4 How machine learning and causal inference can help each other Part 2 5 Finding comparable cases with propensity scores 6 Direct and indirect effects with linear models 7 Dealing with complex graphs 8 Advanced tools with the DoubleML library Part 3 9 Instrumental variables 10 Potential outcomes framework 11 The effect of a time-related event A The math behind the adjustment formula B Solutions to exercises in chapter 2 C Technical lemma for the propensity scores D Proof for doubly robust estimator E Technical lemma for the alternative instrumental variable estimator F Proof of the instrumental variable formula for imperfect compliance.

Codice articolo 9781633439658

Dati bibliografici

• Titolo: Causal Inference for Data Science
• Editore: Manning Publications Jan 2025
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 1633439658
• ISBN 13: 9781633439658
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 234 L x 187 A x 22 P (millimetri) 234 millimetri di larghezza per 187 millimetri di altezza per 22 millimetri di profondità
• Peso: 664 grammi

Descrizione articolo

Riassunto

When you know the cause of an event, you can affect its outcome. This accessible introduction to causal inference shows you how to determine causality and estimate effects using statistics and machine learning.

In Causal Inference for Data Science you will learn how to:

• Model reality using causal graphs
• Estimate causal effects using statistical and machine learning techniques
• Determine when to use A/B tests, causal inference, and machine learning
• Explain and assess objectives, assumptions, risks, and limitations
• Determine if you have enough variables for your analysis

It’s possible to predict events without knowing what causes them. Understanding causality allows you both to make data-driven predictions and also intervene to affect the outcomes. Causal Inference for Data Science shows you how to build data science tools that can identify the root cause of trends and events. You’ll learn how to interpret historical data, understand customer behaviors, and empower management to apply optimal decisions.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
A/B tests or controlled trials are expensive and often unfeasible in a business environment. Causal inference is a powerful methodology that allows a data scientist to identify causes from data, even when no experiment or test has been performed. Using causal methods increases the level of confidence in business decision making by clearly connecting causes and effects.

About the book
Causal Inference for Data Science introduces data-centric techniques and methodologies you can use to estimate causal effects. The book dives into the relationship between causal inference and machine learning and the limitations of both. The practical techniques presented in this unique book are accessible to anyone with intermediate data science skills and require no advanced statistics! The numerous insightful examples show you how to put causal inference into practice in the real world. You’ll assess the performance of advertising platforms, choose the health treatments with the most positive impact, and learn how to approach the delicate art of product pricing from a causal inference perspective.

About the reader
For data scientists, machine learning engineers, statisticians and economists who want to learn a machine learning approach to causal inference.

About the author
Aleix Ruiz de Villa is a freelance data science consultant with a PhD in mathematical analysis from the Universitat Autonoma de Barcelona. Aleix has worked in the journalism, retail, transportation and software development industries. He is the founder of the Barcelona Data Science & Machine Learning Meetup.

Informazioni sull'autore

Aleix Ruiz de Villa is a freelance data science consultant with a PhD in mathematical analysis from the Universitat Autonoma de Barcelona. Aleix has worked in the journalism, retail, transportation and software development industries. He is the founder of the Barcelona Data Science & Machine Learning Meetup.