{"product_id":"artificial-intelligence-and-causal-inference-9780367859404","title":"Artificial Intelligence and Causal Inference","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eArtificial Intelligence and Causal Inference \u003c\/b\u003eaddress the recent development of  relationships between  artificial intelligence\u003cb\u003e (AI) \u003c\/b\u003eand causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination. Understanding, transfer and generalization are major principles that give rise intelligence. One of a key component for understanding is causal inference. Causal inference includes intervention, domain shift learning, temporal structure  and counterfactual thinking as major concepts to understand causation and reasoning. Unfortunately, these essential components of the causality are often overlooked by machine learning, which leads to some failure of the deep learning. AI and causal inference involve (1) using AI techniques as major tools for causal analysis and (2) applying the causal concepts and causal analysis m\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\" Both deep learning and causal inference are fast-moving fields, and the author covers the latest topics and methods well. The book has a high ratio of equations to text, and even more technical material is contained in appendices at the end of each chapter.\"\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eStanley E. Lazic\u003c\/strong\u003e, \u003cem\u003eUniversity of Ottawa\u003c\/em\u003e, Series A: Statisics in Society, 2022.\u003c\/p\u003e\u003cp\u003e\"The book is suitable for use in a graduate-level course on AI. The exercises are challenging but their answers are provided in the end of the book. Not all contents are understandable by the statistics community or commonly useful in the practice of statistics. I enjoyed reading this book. I recommend this book to engineering, data science, predictive business, statistics and computing professionals.\"\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eRamalingam Shanmugam\u003c\/strong\u003e, \u003cem\u003eSchool of Health Administration, Texas State University, San Marcos, Texas, \u003c\/em\u003eJournal of Statistical Computation and Simulation, 2023.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e1. Deep Neural Networks. 2. Deep Wide Neural Networks. 3. Dynamics of Output of Neural Networks. 4. Deep Generative Models. 5. Representation Learning. 5. Graph Representation Learning. 6. Deep Learning for Causal Inference. 7. Deep Learning for Counterfactual Inference and Treatment Estimation. 8. Reinforcement Learning, Meta-Learning for Causal Inference and Quantum Causal Analysis.\u003c\/p\u003e","brand":"Taylor \u0026 Francis Ltd","offers":[{"title":"Default Title","offer_id":51018080485719,"sku":"9780367859404","price":104.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780367859404.jpg?v=1750775571","url":"https:\/\/bookcurl.com\/products\/artificial-intelligence-and-causal-inference-9780367859404","provider":"Book Curl","version":"1.0","type":"link"}