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001484698 001__ 1484698
001484698 003__ OCoLC
001484698 005__ 20240117003333.0
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001484698 019__ $$a1410758882$$a1411307740
001484698 020__ $$a9783031350511$$qelectronic book
001484698 020__ $$a3031350510$$qelectronic book
001484698 020__ $$z3031350502
001484698 020__ $$z9783031350504
001484698 0247_ $$a10.1007/978-3-031-35051-1$$2doi
001484698 035__ $$aSP(OCoLC)1413473042
001484698 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP$$dOCLCO
001484698 049__ $$aISEA
001484698 050_4 $$aQ325.5$$b.M33 2023
001484698 08204 $$a006.31$$223/eng/20231212
001484698 24500 $$aMachine learning for causal inference /$$cSheng Li, Zhixuan Chu, editors.
001484698 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2023]
001484698 300__ $$a1 online resource (357 pages) :$$billustrations (black and white, and color).
001484698 336__ $$atext$$btxt$$2rdacontent
001484698 337__ $$acomputer$$bc$$2rdamedia
001484698 338__ $$aonline resource$$bcr$$2rdacarrier
001484698 5050_ $$aOverview of the Book -- Causal Inference Preliminary -- Causal Effect Estimation: Basic Methodologies -- Causal Inference on Graphs -- Causal Effect Estimation: Recent Progress, Challenges, and Opportunities -- Fair Machine Learning Through the Lens of Causality -- Causal Explainable AI -- Causal Domain Generalization -- Causal Inference and Natural Language Processing -- Causal Inference and Recommendations -- Causality Encourage the Identifiability of Instance-Dependent Label Noise -- Causal Interventional Time Series Forecasting on Multi-horizon and Multi-series Data -- Continual Causal Effect Estimation -- Summary.
001484698 506__ $$aAccess limited to authorized users.
001484698 520__ $$aThis book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative examples, and assumptions. It then delves into the different types of classical causal inference methods, such as matching, weighting, tree-based models, and more. Additionally, the book explores how machine learning can be used for causal effect estimation based on representation learning and graph learning. The contribution of causal inference in creating trustworthy machine learning systems to accomplish diversity, non-discrimination and fairness, transparency and explainability, generalization and robustness, and more is also discussed. The book also provides practical applications of causal inference in various domains such as natural language processing, recommender systems, computer vision, time series forecasting, and continual learning. Each chapter of the book is written by leading researchers in their respective fields. Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system's capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data.
001484698 588__ $$aDescription based on online resource; title from digital title page (viewed on December 13, 2023).
001484698 650_6 $$aApprentissage automatique.
001484698 650_6 $$aInférence (Logique)$$xInformatique.
001484698 650_0 $$aMachine learning.$$vCongresses$$0(DLC)sh2008107143
001484698 650_0 $$aInference$$xData processing.
001484698 650_0 $$aCausation$$xData processing.
001484698 655_0 $$aElectronic books.
001484698 7001_ $$aLi, Sheng,$$eeditor.$$d1828-1906$$0(OCoLC)oca00302866
001484698 7001_ $$aChu, Zhixuan,$$eeditor.
001484698 77608 $$iPrint version:$$tMACHINE LEARNING FOR CAUSAL INFERENCE.$$d[Place of publication not identified] : SPRINGER INTERNATIONAL PU, 2023$$z3031350502$$w(OCoLC)1378365958
001484698 852__ $$bebk
001484698 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-35051-1$$zOnline Access$$91397441.1
001484698 909CO $$ooai:library.usi.edu:1484698$$pGLOBAL_SET
001484698 980__ $$aBIB
001484698 980__ $$aEBOOK
001484698 982__ $$aEbook
001484698 983__ $$aOnline
001484698 994__ $$a92$$bISE