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

Description

Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free 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) 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: Causation is not correlation - Casual Inference and Discovery in Python is a great book - it's approachable, clear, filled with examples and code to follow through. I'm only in the first half of the book, but I feel that it's helping me with my perspective as a data scientict. Causation is not correlation, so this book won't make you better, but there's a good correlation between reading it and feeling like you have more tools to solve real-world problems