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Key benefits

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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Frequently bought together

Causal Inference and Discovery in Python

₹4021.99

50 Algorithms Every Programmer Should Know

₹3723.99

Machine Learning with PyTorch and Scikit-Learn

₹4096.99

Total ₹ 11,842.97

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Sangita Mahala Jul 31, 2023

I highly recommend Causal Inference and Discovery in Python to anyone who wants to learn about causal inference and machine learning in Python. The book is well-written, informative, and easy to follow.

Amazon Verified review

Rohan Singh Rajput Dec 25, 2023

As someone deeply passionate about the intersection of data science and causal inference, I recently finished reading "Causal Inference and Discovery in Python" by Aleksander Molak. It's a rare gem that brilliantly bridges theoretical concepts and practical application, and I can't help but share my thoughts with you all!🌟 Structure & Content:The book is ingeniously divided into three parts. The first section lays a solid foundation in causality, making a compelling case for its importance beyond conventional statistical methods. The chapters on Judea Pearl's Ladder of Causation are particularly enlightening.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. It's a goldmine for practitioners, offering clarity and depth on complex topics.Part 3 is a forward-looking exploration into causal discovery, blending advanced topics like deep learning with practical applications. It's a testament to the book's commitment to staying at the forefront of causal machine learning.👨‍💻 Practicality & Application:What sets this book apart is its seamless blend of theory and practice. The Python code snippets and real-world examples are not just add-ons but core components that make complex ideas tangible and applicable.📖 Writing Style:Molak's writing is clear, engaging, and accessible. He tackles sophisticated concepts with an ease that speaks of his deep understanding and passion for the subject.🌍 Implications for Data Science:As data science continues to evolve, the importance of causal inference only grows. This book is more than just a resource; it's a guide that empowers you to implement causal inference in real-world scenarios, enhancing your analytical toolkit.In conclusion, "Causal Inference and Discovery in Python" is a must-read for anyone in data science, from students to seasoned professionals. Whether you're looking to deepen your understanding of causal inference or apply it in Python, this book is an invaluable asset.#DataScience #CausalInference #Python #MachineLearning #BookReview #AnalyticsCommunity👉 Highly recommend grabbing a copy if you're looking to stay ahead in the ever-evolving field of data science! 🌐📊🐍

Judith Hurwitz Sep 11, 2023

I was eagerly anticipating this book because the author is so knowledgable about the technology behind causal AI and causal Inference. I was not disappointed. The book is very well written and provides the right level of business and technical understanding of an important but complex area. I strongly recommend this important book.

Kelvin D. Meeks Jul 31, 2023

Why should you want to read this book?Understand the landscape of causality modeling – and a broad survey of the many available tools – as well as the historical background - and fairly recent trends - in causality modeling research.To learn the reason to use causal modeling rather than traditional machine learning.Understand the libraries and algorithms available for causal modeling.Understand the use cases (and limitations) of different causal modeling strategies and capabilities.Understand the importance (and challenges) of obtaining Expert Knowledge for causal modeling.Understand the practical aspects of applying and implementing causality modeling to business problems.This book will save you months of effort trying to research and assemble the equivalent information. The book offers an optimal and efficient path to learning the information.What I particularly liked:- Breadth and depth of the treatment of the material.- Inclusion of interesting examples – written in Python.- Use of Jupyter notebooks- The excellent quality of the images for source code and graphics.- Inclusion of the link to the companion github repository for the book- QR code to download a PDF version of the book- Inclusion of a References section at the end of chapters, with suggested additional reading. This *really* enriches a book for me – and indicates that the author has given deep thought to how to expand and elevate the reader’s understanding of the material.- The treatment and depth of the material covered – is exceptionally well-done.- The writing is ACCESSIBLE. The author invests sufficient effort to bring the reader along – and build their knowledge with successive layering of concepts.- The material covered is fascinating - well-researched - and every single chapter is filled with rich content - as well as extensive citations in the References section, at the end of each chapter, with *numerous* high quality suggestions for additional reading.- The inclusion of excellent coding examples.- The writing is exceptionally well-organized, CRISP (a word I reserve for only the very best writing) – and weaves concepts, theory, and practical hands-on coding exercises – into a seamless narrative.- The writing is fresh – and lively – filled with insights and examples that are meaningful to any reader interested in Causality Inference/Discovery/Modeling, Machine Learning, and Deep Learning.- Near the end of the book, I came to a new appreciation for the care with which the author has taken to prepare and organize the information – in successive elegant layers – to enrich the reader’s learning experience. This is another confirmation of the exceptional quality of the writing – and the author’s depth of preparation to write this book.Taken as a whole (the combination of the writer’s content – and the cornucopia of suggested follow-up references) – this book easily wins, hands-down, as the best book I’ve ever read, published by Packt.

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

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