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Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and more

Discover modern causal inference techniques for average and heterogenous treatment effect estimation

Explore and leverage traditional and modern causal discovery methods

Description

Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality. You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more. By the end of this book, you will be able to build your own models for causal inference and discovery using statistical and machine learning techniques as well as perform basic project assessment.

Who is this book for?

This book is for machine learning engineers, researchers, and data scientists looking to extend their toolkit and explore causal machine learning. It will also help people who’ve worked with causality using other programming languages and now want to switch to Python, those who worked with traditional causal inference and want to learn about causal machine learning, and tech-savvy entrepreneurs who want to go beyond the limitations of traditional ML. You are expected to have basic knowledge of Python and Python scientific libraries along with knowledge of basic probability and statistics.

What you will learn

Master the fundamental concepts of causal inference

Decipher the mysteries of structural causal models

Unleash the power of the 4-step causal inference process in Python

Explore advanced uplift modeling techniques

Unlock the secrets of modern causal discovery using Python

Use causal inference for social impact and community benefit

What do you get with Print?

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Colour book shipped to your preferred address

Redeem a companion digital copy on all Print orders

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Frequently bought together

Causal Inference and Discovery in Python

$53.99

Machine Learning with PyTorch and Scikit-Learn

$54.99

50 Algorithms Every Programmer Should Know

$49.99

Total $ 158.97

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valdez ladd Nov 27, 2023

Excellent book that show theory with practical examples and generous online references in the book.that avoids overloading the size and mission of the teachings

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N/A Feb 01, 2024

Excellent book! With many practical examples that can be used as an initial guide to apply causal inference to real problems. The best causal inference book I've ever read (even better than "Causal Inference: for the Brave and True"!)

Matthew Emerick Jul 18, 2023

I was very impressed by this book. I can't count myself as a machine learning expert by any means, and this book was written with them in mind, but I can still work through and understand the concepts of causal inference.This book does a wonderful job of starting with the basic concept and building on that. It's fully hands-on and connects everything to statistical machine learning. However, it will be rough reading for someone just interested in ML and not fully working on it. This is a highly technical book for a highly technical concept.An excellent book that adds an important tool to your machine-learning toolbox.

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John Feb 19, 2024

This book stands out for its exceptional organization and practical approach, particularly notable for the inclusion of code shared on GitHub. This feature significantly enhances the book's accessibility, allowing developers to easily run and evaluate the book's concepts and promises. The integration of code with the written content bridges the gap between theory and practice, providing a hands-on experience that is both educational and engaging. This approach not only demonstrates the authors' commitment to transparency and practicality but also empowers readers to directly interact with the material, fostering a deeper understanding and appreciation of the subject matter.

John Jun 10, 2023

Highly recommended for beginners and those who want to start empirical analysis using DoWhy

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

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