---
product_id: 533157307
title: "Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more"
price: "€ 81.43"
currency: EUR
in_stock: true
reviews_count: 13
url: https://www.desertcart.pt/products/533157307-causal-inference-and-discovery-in-python-unlock-the-secrets-modern
store_origin: PT
region: Portugal
---

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

**Price:** € 81.43
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- **What is this?** Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
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## Description

Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more [Aleksander Molak] on desertcart.com. *FREE* shipping on qualifying offers. Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more

Review: Unlocking the Power of Causal Inference - Causal Inference and Discovery in Python is a valuable addition to the library of Data Scientists and researchers who are interested in Causal Inference. This book offers a comprehensive and practical guide to causal inference and discovery methods. The book starts with a solid foundation by explaining the fundamentals of causal inference and how it differs from Machine Learning. It takes readers through the concepts of causality, counterfactuals, direct acyclic graphs and causal discovery, making these complex ideas clear and understandable to a wide audience, from beginners to seasoned data scientists. The practical examples, along with clear explanations and code snippets, make it easy for readers to follow along and apply what they've learned. What sets this book apart is its strong emphasis on hands-on implementation. The author provides numerous real-world examples and practical exercises using Python libraries such as EconML, doWhy, gCastle. and Causica. These libraries enable readers to implement causal analysis techniques efficiently, which is essential for anyone looking to apply causal inference in their data projects. Another notable feature of the book is its attention to potential pitfalls and challenges in causal analysis. It doesn't just stop at teaching the "how" but also delves into the "why" behind certain methodologies and the limitations of causal inference techniques. This level of depth and transparency is essential for building a deep understanding of the subject matter. The book also covers advanced topics like causal discovery algorithms, providing readers with a well-rounded overview for this particular area. While this book is a valuable resource for anyone interested in causal inference, it may not be suitable for absolute beginners in Python. Some prior familiarity with Python programming and basic data science concepts is recommended to fully grasp the content. In summary, Causal Inference and Discovery in Python is a commendable resource for those looking to demystify the complexities of causal analysis and apply it to real-world problems. Its hands-on approach, coupled with clear explanations and practical examples, makes it a must-read for data scientists, researchers, and analysts seeking to understand and leverage causal inference in Python.
Review: Great resource to learn causal inference - This book is helping me understand the fundamental concepts of causal inference and the various application methods. My journey started with standard statistics, then to bayes, and now causal models. The practical, hands-on book exercises clarified and cemented the many new (to me) concepts unique to causal modeling. I appreciate Mr. Molak taking time to write this excellent book.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #153,738 in Books ( See Top 100 in Books ) #5 in Computer Programming Logic #123 in Python Programming #343 in Artificial Intelligence & Semantics |
| Customer Reviews | 4.5 4.5 out of 5 stars (151) |
| Dimensions  | 7.5 x 1.05 x 9.25 inches |
| ISBN-10  | 1804612987 |
| ISBN-13  | 978-1804612989 |
| Item Weight  | 1.71 pounds |
| Language  | English |
| Print length  | 456 pages |
| Publication date  | May 31, 2023 |
| Publisher  | Packt Publishing |

## Images

![Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more - Image 1](https://m.media-amazon.com/images/I/71itCI1Uv0L.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ Unlocking the Power of Causal Inference
*by R***. on October 2, 2023*

Causal Inference and Discovery in Python is a valuable addition to the library of Data Scientists and researchers who are interested in Causal Inference. This book offers a comprehensive and practical guide to causal inference and discovery methods. The book starts with a solid foundation by explaining the fundamentals of causal inference and how it differs from Machine Learning. It takes readers through the concepts of causality, counterfactuals, direct acyclic graphs and causal discovery, making these complex ideas clear and understandable to a wide audience, from beginners to seasoned data scientists. The practical examples, along with clear explanations and code snippets, make it easy for readers to follow along and apply what they've learned. What sets this book apart is its strong emphasis on hands-on implementation. The author provides numerous real-world examples and practical exercises using Python libraries such as EconML, doWhy, gCastle. and Causica. These libraries enable readers to implement causal analysis techniques efficiently, which is essential for anyone looking to apply causal inference in their data projects. Another notable feature of the book is its attention to potential pitfalls and challenges in causal analysis. It doesn't just stop at teaching the "how" but also delves into the "why" behind certain methodologies and the limitations of causal inference techniques. This level of depth and transparency is essential for building a deep understanding of the subject matter. The book also covers advanced topics like causal discovery algorithms, providing readers with a well-rounded overview for this particular area. While this book is a valuable resource for anyone interested in causal inference, it may not be suitable for absolute beginners in Python. Some prior familiarity with Python programming and basic data science concepts is recommended to fully grasp the content. In summary, Causal Inference and Discovery in Python is a commendable resource for those looking to demystify the complexities of causal analysis and apply it to real-world problems. Its hands-on approach, coupled with clear explanations and practical examples, makes it a must-read for data scientists, researchers, and analysts seeking to understand and leverage causal inference in Python.

### ⭐⭐⭐⭐⭐ Great resource to learn causal inference
*by J***E on April 15, 2025*

This book is helping me understand the fundamental concepts of causal inference and the various application methods. My journey started with standard statistics, then to bayes, and now causal models. The practical, hands-on book exercises clarified and cemented the many new (to me) concepts unique to causal modeling. I appreciate Mr. Molak taking time to write this excellent book.

### ⭐⭐⭐⭐⭐ Lovely graduate level textbook
*by S***I on December 7, 2025*

Good textbook. In perfect condition

## Frequently Bought Together

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*Store origin: PT*
*Last updated: 2026-03-20*