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Nº de artículo:
82293980
Causal Inference for Data Science
87% of respondents would recommend this to a friend
Nº de artículo:
82293980
UYU 3558
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Understanding causality allows you both to make data-driven predictions and also intervene to affect the outcomes.
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What Stands Out
Rigorous Methodology
Employs robust statistical techniques ensuring reliable conclusions. This methodological rigor differentiates it from simpler statistical approaches, making it ideal for data scientists aiming for precision in causal analysis.
Practical Applications
Focuses on real-world scenarios that data scientists encounter, bridging theory and practice effectively. This ensures users can apply concepts directly to their own data challenges.
Comprehensive Resources
Offers extensive resources, including case studies and datasets, enhancing learning opportunities. This resource-rich approach supports skilled practitioners in mastering causal inference comprehensively.
Detalles de producto
- 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 graphsEstimate causal effects using statistical and machine learning techniquesDetermine when to use A/B tests, causal inference, and machine learningExplain and assess objectives, assumptions, risks, and limitationsDetermine if you have enough variables for your analysisIt’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.About the technologyWhy 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 bookCausal 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 insideWhen to use A/B tests, causal inference, and MLAssess objectives, assumptions, risks, and limitationsApply causal inference to real business dataAbout the readerFor data scientists, ML engineers, and statisticians.About the authorAleix Ruiz de Villa Robert is a data scientist with a PhD in mathematical analysis from the Universitat Autònoma de Barcelona.Table of ContentsPart 11 Introducing causality2 First steps: Working with confounders3 Applying causal inference4 How machine learning and causal inference can help each otherPart 25 Finding comparable cases with propensity scores6 Direct and indirect effects with linear models7 Dealing with complex graphs8 Advanced tools with the DoubleML libraryPart 39 Instrumental variables10 Potential outcomes framework11 The effect of a time-related eventA The math behind the adjustment formulaB Solutions to exercises in chapter 2C Technical lemma for the propensity scoresD Proof for doubly robust estimatorE Technical lemma for the alternative instrumental variable estimatorF Proof of the instrumental variable formula for imperfect compliance
| Publisher | Manning |
| Publication date | January 21, 2025 |
| Language | English |
| Print length | 392 pages |
| ISBN-10 | 1633439658 |
| ISBN-13 | 978-1633439658 |
| Item Weight | 2.31 pounds (1.05 kg) |
| Dimensions | 7.38 x 1 x 9.25 inches (18.7 x 2.5 x 23.5 cm) |
Who Should Buy?
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Data Scientists
Ideal for data scientists looking to deepen their understanding of causality and improve their analytical skills.
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Researchers
Beneficial for researchers needing causal analysis techniques to bolster their exploratory studies and published results.
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Statisticians
Useful for statisticians aiming to enhance their causal modeling capabilities and apply them in real-world scenarios.
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Beginners in Statistics
Not suitable for those with no statistical background, as it may be too advanced and complex to grasp.
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características y beneficios
- Learn to determine causality and estimate effects with statistical and machine learning techniques.
- Access practical methodologies even without experiments or A/B testing.
- Gain insights into customer behaviors and enhance decision-making skills.
- Apply causal reasoning to ordinary business scenarios for optimal outcomes.
- Utilize Directed Acyclic Graphs (DAGs) for assessing advertising performance and more.
- Purchase includes free eBook in PDF and ePub formats.
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