Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more

Analyzed on

112Total Reviews

Computer Science

C

Company Review Grade:

Aleksander Molak

2 Total Products

Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning...

Analyzed on

Sold by Aleksander Molak

C

Fakespot review grade

C

This analysis is outdated. Please reanalyze.

Ask Fakespot Chat questions about this productAlpha

Initializing Fakespot Chat

System error,

Loading suggested questions

Suggested Questions

No suggested questions were found

Please note: The content is generated by an AI language model based on reviews. We are constantly improving the accuracy, however, its outputs may contain errors or offensive content from certain reviews. Please report any errors or offensive content to us. Also, please note that we will not collect your questions unless you click the thumbs up/down button to provide feedback. The answers displayed will be stored by us so if another Fakespotter asks a similar question we can provide them an answer faster. Your feedback will only help us make Fakespot Chat better! By using Fakespot Chat, you agree to Fakespot's terms of use and privacy notice.

Pros & Cons

Update Pros and Cons

The AI used to provide these results are constantly improving. These results might change.

Pros

The book is well-written and easy to follow, making it an excellent resource for both beginners and experienced practitioners. The intellectual depth of the work goes beyond mere technical instruction; it inspires deeper contemplation about the subjec... Read More

The book unfolds systematically, seamlessly blending theoretical concepts with practical implementation using python. The python code snippets and real-world examples are not just add-ons but core components that make complex ideas tangible and applic... Read More

The first section lays a solid foundation in causality, making a compelling case for its importance beyond conventional statistical methods. Chapter 3 and 4 goes into discovering causal relationship using regression and graphical representation.

The heart of the book lies in part 2, where molak delves into the nuts and bolts of causal inference, integrating python tools like dowhy and econml. If you want to learn more about causal inference in python, visit datum.

Cons

On kindle for mac formulas are not rendering correctly i. E. They are not showing up on the screen. For example, if you are trying to write a formula on a computer, the screen will not show up. This is because the computer is not using the correct ope... Read More

Was this feature helpful?

Highlights

The AI used to provide these results are constantly improving. These results might change.

Quality

It covers a wide range machine and deep leagning approaches.

Beginning with an elucidation of causality's essence and significance in machine learning, molak lays the foundation for a deep... Read More

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

Overview

How are reviewers describing this item?
causal, practical, comprehensive and theoretical.

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 66.4% of the reviews are reliable.

This product had a total of 112 reviews as of our last analysis date on Sep 25 2024.