Product guide

Causal Inference and Discovery in Python

Causal Inference and Discovery in Python is a practical book about applying causal reasoning and machine learning with Python. It moves from structural causal models, interventions, and counterfactuals to treatment-effect estimation and causal discovery, with exercises and code. Readers are expected to know basic Python, scientific libraries, probability, and statistics.

At a glance

Introduces Pearlian concepts including structural causal models, interventions, and counterfactuals.
Covers average and heterogeneous treatment-effect estimation, including modern machine-learning approaches.
Explores causal discovery methods and graphical models.
Pairs theoretical explanations with practical exercises and Python code.
Includes topics ranging from a four-step causal-inference process to deep learning and NLP applications.

Price

$24.83Save 54%

Reference price: ~~$53.99~~

Price checked: Nov 14, 2026

View offer Ahead of Black FridayExplore the selection

Often bought together

Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

$32.78

Deep Reinforcement Learning Hands-On: A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF

$29.65

Pros and cons

Pros

The scope connects causal foundations with practical estimation and discovery workflows.
Python examples and exercises give readers material to apply alongside the conceptual explanations.
The contents include both traditional causal methods and newer machine-learning topics.

Technical specifications

8 specifications

Publication details
Publisher	Packt Publishing
Publication date	May 31, 2023
Language	English
Print length	456 pages
ISBN-10	1804612987
ISBN-13	978-1804612989

Dimensions and installation
Weight listed	1.71 pounds
Dimensions	7.5 x 1.05 x 9.25 inches

What to know before buying

Listing details to confirm

The listing expects basic Python and familiarity with scientific libraries, probability, and statistics. The title mentions DoWhy, EconML, and PyTorch, while the detailed description does not specify version requirements or a complete environment setup.

Who is it for?

A good fit for

Data scientists and machine-learning engineers seeking to add causal methods to their Python toolkit.
Researchers interested in causal inference, treatment-effect estimation, or causal discovery.
Readers with causal-inference experience in another language who want to work with Python.

Less suited to

Readers without basic Python, scientific-library, probability, and statistics knowledge.
Readers seeking a general introduction to machine learning rather than a focused book on causality.

Customer reviews on Amazon

4.4 / 5 · 162 ratings

5 stars77%77%
4 stars8%8%
3 stars5%5%
2 stars3%3%
1 star7%7%

Aggregate ratings lean positive, with most ratings in the top category, but a smaller spread across lower star categories means experiences are not uniformly positive. These figures do not reveal what readers liked or disliked, so they are limited evidence about how well the book explains its stated Python and causal-method topics.

Frequently asked questions

What background does the book expect?

The listing says readers should have basic Python and Python scientific-library knowledge, along with basic probability and statistics.

What topics does it cover?

It covers causal models, interventions, counterfactuals, treatment-effect estimation, causal discovery, and applications involving deep learning and NLP.

Does it include practical Python work?

The listing describes theoretical explanations alongside practical exercises and Python code.