Causal Inference for Data Science

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Beschreibung

<b>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.</b><br><br>A/B tests or randomized controlled trials are expensive and often unfeasible in a business environment. <i>Causal Inference for Data Science</i> reveals the techniques and methodologies you can use to identify causes from data, even when no experiment or test has been performed.<br><br>In <i>Causal Inference for Data Science</i> you will learn how to:<ul><li>Model reality using causal graphs</li><li>Estimate causal effects using statistical and machine learning techniques</li><li>Determine when to use A/B tests, causal inference, and machine learning</li><li>Explain and assess objectives, assumptions, risks, and limitations</li><li>Determine if you have enough variables for your analysis</li></ul>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. <i>Causal Inference for Data Science</i> 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.<br><br><b>About the technology</b><br><br>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.<br><br><b>About the book</b><br><br><i>Causal Inference for Data Science</i> 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 inferenc

Artikeldetails

EAN

9781633439658

Sprache

Englisch

Einband / Art

Taschenbuch

Maße

234 x 187 x 22 mm

Erscheinungsjahr

2025

Verlag / Hersteller

Manning Publications