Wow! eBook
Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
Author(s): Aleksander Molak (Author)
- Publisher: Packt Publishing
- Publication Date: 31 May 2023
- Language: English
- Print length: 456 pages
- ISBN-10: 1804612987
- ISBN-13: 9781804612989
Book Description
Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data
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Key Features
- 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
Book 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.
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
Who this book is 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.
Table of Contents
- Causality – Hey, We Have Machine Learning, So Why Even Bother?
- Judea Pearl and the Ladder of Causation
- Regression, Observations, and Interventions
- Graphical Models
- Forks, Chains, and Immoralities
- Nodes, Edges, and Statistical (In)dependence
- The Four-Step Process of Causal Inference
- Causal Models – Assumptions and Challenges
- Causal Inference and Machine Learning – from Matching to Meta- Learners
- Causal Inference and Machine Learning – Advanced Estimators, Experiments, Evaluations, and More
- Causal Inference and Machine Learning – Deep Learning, NLP, and Beyond
- Can I Have a Causal Graph, Please?
(N.B. Please use the Read Sample option to see further chapters)
Editorial Reviews
Review
“Despite causality becoming a key topic for AI and increasingly also for generative AI, most developers are not familiar with concepts such as causal graphs and counterfactual queries. Aleksander’s book makes the journey into the world of causality easier for developers. The book spans both technical concepts and code and provides recommendations for the choice of approaches and algorithms to address specific causal scenarios. This book is comprehensive yet accessible. Machine learning engineers, data scientists, and machine learning researchers who want to extend their data science toolkit to include causal machine learning will find this book most useful. Looking to the future of AI, I find the sections on causal machine learning and LLMs especially relevant to both readers and our work.”
—
Ajit Jaokar, Visiting Fellow, Department of Engineering Science, University of Oxford, and Course Director, Artificial Intelligence: Cloud and Edge Implementations, University of Oxford
“My exploration of causal analysis began roughly 5 years ago during a stimulating conversation with Vint Cerf. When questioned about the foremost challenge for ML in physics, my immediate response was – causality. In many areas of physics and materials science, we often grapple with multiple observations, yet opportunities for experimental intervention are sparse. While we can change reagents used in synthesis of material, we cannot experiment with the nature of physical laws controlling the interactions between atoms – even though we can visualize them. While classical ML primarily focuses on correlation, genuine insights in experimental domains are anchored in grasping the causal relationships between observables and their temporal dynamics. Vint pointed me towards Judea Pearl’s pioneering work. While Pearl’s contributions are profoundly enlightening, their pragmatic applications, especially in materials discovery or in interpreting microscopic observations, felt elusive to me and my colleagues. With an array of methods scattered across diverse publications, books, and fragmented GitHub repositories, finding a direct, actionable solution was akin to navigating a maze. The Aleksander Molak’s book on Causal Inference and Discovery in Python emerged as a beacon. Molak masterfully intertwines theory with hands-on code implementations. His work represents the comprehensive causality guide I’ve sought for the past half-decade – a singular resource allowing me to delve into the text and immediately apply the code to tangible challenges in materials science. For this gem, my gratitude knows no bounds.”
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Sergei V. Kalinin, Weston Fulton professor, Department of Materials Science and Engineering
“I got my start into the world of causal modeling through System Dynamics a couple of decades ago and that led to reading Pearl’s book and papers. I have seen very slow adoption of these ideas despite their tremendous promise, and I think this book will help a great deal. The first part has a lot of introductory material that is great for beginners, but you can skip if you are familiar with Pearl’s work. The second part is really the core of the book that covers a lot of Python packages and their use to do causal analysis, including LLMs. The third part gets into causal discovery that is very critical for solving practical problems and here again a lot of ideas and packages are covered. One thing that is missing is detailed practical examples, which are still needed for mainstream adoption. I look forward to the day when the author publishes this next book of examples. Overall, a must-read book for any data scientist.”
—
Bipin Chadha, VP Data Science – CSAA Insurance Group, a AAA Insurer
About the Author
Aleksander Molak is an independent machine learning researcher and consultant. Aleksander gained experience working with Fortune 100, Fortune 500, and Inc. 5000 companies across Europe, the USA, and Israel, helping them to build and design large-scale machine learning systems. On a mission to democratize causality for businesses and machine learning practitioners, Aleksander is a prolific writer, creator, and international speaker. As a co-founder of Lespire.io, an innovative provider of AI and machine learning training for corporate teams, Aleksander is committed to empowering businesses to harness the full potential of cutting-edge technologies that allow them to stay ahead of the curve.
This book has been co-authored by many people whose ideas, love, and support left a significant trace in my life. I am deeply grateful to each one of you.
