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Causal Inference: The Mixtape
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Causal Inference: The Mixtape is an accessible, contemporary introduction to the methods for determining cause and effect in the social sciences.
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What Stands Out
產品詳情
- Causal Inference: The Mixtape is a comprehensive guide to causal inference in data science
- Explore key concepts such as confounding, selection bias, and more
- Written by prominent experts in the field of causal inference
- Includes practical examples and case studies to illustrate concepts
- Ideal for data scientists, researchers, and analysts looking to enhance their causal inference skills
- Offers valuable insights and techniques for drawing causal conclusions from observational data
| Publisher | Yale University Press |
| Publication date | January 26, 2021 |
| Language | English |
| Print length | 584 pages |
| ISBN-10 | 0300251688 |
| ISBN-13 | 978-0300251685 |
| Item Weight | 1.5 pounds (680 grams) |
| Dimensions | 5.5 x 1.17 x 8.5 inches (14 x 3 x 21.6 cm) |
Who Should Buy?
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Students and Learners
Ideal for students majoring in statistics or economics who want a practical understanding of causal inference methods.
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Data Scientists
Useful for data scientists looking to deepen their skills in causal analysis to enhance their predictive modeling capabilities.
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Researchers
Beneficial for researchers in social sciences who require a solid foundation in causal inference for empirical studies.
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Casual Readers
Not suited for casual readers without a strong quantitative background or interest in statistical methodologies.
產品敘述
Causal Inference: The Mixtape
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Probability & Statistics Editorial Review
The "Causal Inference: The Mixtape" is a book that focuses on providing information on different technical aspects and algorithms of causal analysis. The book is ideal for students in applied economics and other quantitative social science programs that prioritize empirics over theory and deeply mathematical derivations. It provides a comprehensive look at various approaches to causal inference with great examples and datasets using Stata and R. The author's attention to detail is much appreciated, and the writing style is friendly. The book is a quick and useful read, especially Considering the subject. However, there are some downsides to the book, including the unnecessarily complicated math and limited use cases, which some readers found unusable if they were trying to apply the concepts in the real world. Additionally, there were issues with typos, where several were found in the math sections. Overall, the book is a perfect source of reference for practitioners in policy practice and economic analysis.
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優點
- Comprehensive Look at Various Approaches to Causal Inference
- Great Examples and Datasets Using Stata and R
- Author's Attention to Detail
- Friendly Writing Style
- Useful Read
缺點
- Complicated Math
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特色和優勢
- Causal inference helps establish the causes and effects of the actions being studied.
- Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers.
- The book uses real-world examples to make the subject thought-provoking.
- It covers a range of modeling techniques and coding instructions for both the R and the Stata programming languages.
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