{"product_id":"9783031983443","title":"Computer Science Foundations and Applied Logic: Foundations, Algorithms and Applications","description":"\u003ch1\u003eComputer Science Foundations and Applied Logic: Foundations, Algorithms and Applications\u003c\/h1\u003e \u003ch2\u003eSucar, Luis Enrique\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis book presents an overview of \u003cstrong\u003e\u003cem\u003ecausal discovery\u003c\/em\u003e\u003c\/strong\u003e, an emergent field with important developments in the last few years, and multiple applications in several fields.\u003c\/p\u003e\n\u003cp\u003eThe book is divided into three parts. The first part provides the necessary background on causal graphical models and causal reasoning. The second describes the main algorithms and techniques for causal discovery: (a) causal discovery from observational data, (b) causal discovery from interventional data, (c) causal discovery from temporal data, and (d) causal reinforcement learning. The third part provides several examples of causal discovery in practice, including applications in biomedicine, social sciences, artificial intelligence and robotics.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eTopics and features:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003c!-- [if !supportLists]--\u003eIncludes the necessary background material: a review of probability and graph theory, Bayesian networks, causal graphical models and causal reasoning\u003c\/li\u003e\n\u003cli\u003eCovers the main types of causal discovery: learning from observational data, learning from interventional data, and learning from temporal data\u003c\/li\u003e\n\u003cli\u003e\n\u003c!-- [if !supportLists]--\u003eIllustrates the application of causal discovery in practical problems\u003c\/li\u003e\n\u003cli\u003e\n\u003c!-- [if !supportLists]--\u003eIncludes some of the latest developments in the field, such as continuous optimization, causal event networks, causal discovery under subsampling, subject specific causal models, and causal reinforcement learning\u003c\/li\u003e\n\u003cli\u003e\n\u003c!-- [if !supportLists]--\u003eProvides chapter exercises, including suggestions for research and programming projects\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eThis book can be used as a textbook for an advanced undergraduate or a graduate course on causal discovery for students of computer science, engineering, social sciences, etc. It can also be used as a complement to a course on causality, together with another text on causal reasoning. It could also serve as a reference book for professionals that want to apply causal models in different areas, or anyone who is interested in knowing the basis of these techniques.\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003eThe intended audience are students and professionals in computer science, statistics and\u003c\/p\u003e\n\u003cp\u003eengineering who want to know the principles of causal discovery and \/ or applied them in different\u003c\/p\u003e\n\u003cp\u003edomains. It could also be of interest to students and professionals in other areas who want to apply\u003c\/p\u003e\n\u003cp\u003ecausal discovery, for instance in medicine and economics.\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Birkhäuser\u003c\/p\u003e \u003cp\u003ePublication Date: 2025-10-28\u003c\/p\u003e \u003cp\u003eFormat: Hardcover\u003c\/p\u003e \u003cp\u003eISBN-13: 9783031983443\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-3-031-98345-0\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 228\u003c\/p\u003e ","brand":"Springer Nature Switzerland","offers":[{"title":"Default Title","offer_id":44309715648652,"sku":"9783031983443","price":89.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783031983443.jpg?v=1774536479","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9783031983443","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}