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Causal Inference: The Mixtape
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Product Details
| 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.
Product Description
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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Pros
- 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
Cons
- Complicated Math
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