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Pros & Cons
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Logical Chapter Progression
Customers are highly impressed with the logical progression of chapters in the book, which builds upon previous concepts and provides practical exercises to reinforce theoretical understanding. The focus on lesser-known libraries like EconML, DoWhy, and DiCE sets this resource apart, making it a valuable tool for data professionals seeking to master causal machine learning.
Transparent And Practical Approach
Customers are highly impressed by the transparent and practical approach of this book on Causal Inference and Discovery in Python. They appreciate the authors' commitment to clear explanations, real-world examples, and hands-on implementation using standard Python packages. The book is well-structured, engaging, and accessible for both beginners and experienced practitioners.
Covers Pearlian Theories
Customers are highly impressed with the book's comprehensive coverage of Pearlian theories in causal machine learning. They praise its clear and practical explanations, accompanied by numerous examples and hands-on exercises using Python libraries like DoWhy, EconML, and PyTorch.
Causal Discovery Algorithms Comparison
Customers are highly impressed with the book's comprehensive coverage of causal discovery algorithms, as well as its seamless blend of theoretical concepts and practical applications using Python code snippets and real-world illustrations. The text offers a valuable resource for machine learning engineers, data scientists, and researchers seeking to master modern causal machine learning.
Highlights
Quality
- use of jupyter notebooks- the excellent quality of the images for source code and graphics.
- the treatment and depth of the material covered – is exceptionally well-done.
This issue is brushed over in this chapter, admittedly due to this being an active area of research and not many tools available.
These concepts are generally difficult to understand using standard text — i would recommend anyone starting to work in causali... Read More
Competitiveness
Moreover, molak's insightful exploration extends to tech-savvy entrepreneurs seeking to elevate their products beyond conventio... Read More
The book also explores the mechanics of how “causes leave traces” and draws comparisons between the main families of causal dis... Read More
Furthermore, the book delves into the mechanics of causal discovery, shedding light on how "causes leave traces" and comparing ... Read More
No wonder that llms and other models built into ai create biases and produce false statements about the world.
Overview
- How are reviewers describing this item?
causal, practical and complex. - Our engine has detected that the listing/variation has anomalous review count history. Please review the historical review count graph.
- Our engine has profiled the reviewer patterns and has determined that there may be deception involved.
- Our engine has determined that the review content quality is low.
- Our engine has analyzed and discovered that 65.3% of the reviews are reliable.
- This product had a total of 123 reviews as of our last analysis date on Feb 9 2025.
Helpful InsightsBETA
Posted by a reviewer on Amazon
The book needs more information on getting started with python for non python users
Posted by a reviewer on Amazon
Beforre i came across this book i thought that the only was to causal inference was to conduct expensive experimental settings
Posted by a reviewer on Amazon
Chapter 1 delves deeper into the issue of “correlation is not causation”
Posted by a reviewer on Amazon
Chapter 2 takes on the esoteric concepts such as association interventions and counterfactual reasoning
Posted by a reviewer on Amazon
These concepts are generally difficult to understand using standard text — i would recommend anyone starting to work in causality to start from this chapter
Posted by a reviewer on Amazon
My personal issue with these frameworks is that they do not handle certain causal relationships
Posted by a reviewer on Amazon
This issue is brushed over in this chapter admittedly due to this being an active area of research and not many tools available
Posted by a reviewer on Amazon
However interested readers can find some references in this chapter which can help them better understand the complexity of the topic
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