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This book provides a comprehensive exploration of causal inference, specifically tailored for machine learning practitioners. It begins by establishing the fundamental distinction between correlation and causation, emphasizing why traditional machine learning models primarily focused on pattern recognition often fall short in scenarios that require an understanding of cause and effect. The book introduces core causal concepts, such as interventions and counterfactuals, and explains how these ideas are formalized through tools like causal graphs (Directed Acyclic Graphs, or DAGs) and the do-operator. Readers will learn to identify common pitfalls in observational data, including confounding, selection bias, and Simpson s Paradox, and will understand why these challenges necessitate a causal approach.
Causal Inference for Machine Learning Engineers: A Practical Guide then moves to practical methods for causal estimation, detailing techniques such as regression adjustment, propensity score methods (including matching, stratification, and inverse probability weighting), and instrumental variables. The book delves into advanced topics such as mediation analysis, causal discovery algorithms (PC and FCI), and transportability, providing a roadmap for applying causal reasoning in diverse real-world applications across healthcare, economics, and the social sciences. A significant portion is dedicated to integrating causal inference with deep learning, introducing architectures such as TARNet, CFRNet, and DragonNet, as well as frameworks like Double Machine Learning, all designed to address the challenges of high-dimensional data and improve causal effect estimation in complex settings.
Sommario
.- Introduction to Causal Thinking.
.- Treatments, Outcomes, and Confounding: Core Concepts.
.- Causal Estimation Basics.
.- Causal Graphs: Structure and Assumptions.
.- Interventions and Counterfactuals.
.- Introduction to Do-Calculus.
.- Backdoor and Frontdoor Criteria.
.- Advanced Causal Inference Methods.
.- Causal Inference Meets Deep Learning.
.- Simulating Causal Data and Evaluation Met rics.
.- Balancing Representations with Causal Deep Learning (CFRNet).
.- Propensity Scores in Causal Deep Learning.
.- Evaluating Causal Models Without Counter factuals.
.- Advanced Topics in Causal Inference.
.- Assumptions and Real-World Challenges in Causal Inference.
.- Summary of Key Concepts.
.- Case Studies.
.- Solutions to Exercises.
Info autore
Durai Rajamanickam is a distinguished AI and data science leader with over two decades of experience, specializing in the application of machine learning to critical real-world challenges in healthcare, finance, and legal technology. Renowned for his ability to distill complex theoretical concepts into actionable solutions, he has spearheaded transformative AI initiatives across various industries.
Riassunto
This book provides a comprehensive exploration of causal inference, specifically tailored for machine learning practitioners. It begins by establishing the fundamental distinction between correlation and causation, emphasizing why traditional machine learning models—primarily focused on pattern recognition—often fall short in scenarios that require an understanding of cause and effect. The book introduces core causal concepts, such as interventions and counterfactuals, and explains how these ideas are formalized through tools like causal graphs (Directed Acyclic Graphs, or DAGs) and the do-operator. Readers will learn to identify common pitfalls in observational data, including confounding, selection bias, and Simpson’s Paradox, and will understand why these challenges necessitate a causal approach.
Causal Inference for Machine Learning Engineers: A Practical Guide then moves to practical methods for causal estimation, detailing techniques such as regression adjustment, propensity score methods (including matching, stratification, and inverse probability weighting), and instrumental variables. The book delves into advanced topics such as mediation analysis, causal discovery algorithms (PC and FCI), and transportability, providing a roadmap for applying causal reasoning in diverse real-world applications across healthcare, economics, and the social sciences. A significant portion is dedicated to integrating causal inference with deep learning, introducing architectures such as TARNet, CFRNet, and DragonNet, as well as frameworks like Double Machine Learning, all designed to address the challenges of high-dimensional data and improve causal effect estimation in complex settings.
