Übersicht			Auf mobile öffnen

Lieferstatus:	i.d.R. innert 7-14 Tagen versandfertig
Veröffentlichung:	November 2023
Genre:	Naturwissensch., Medizin, Technik
	CausalDiscovery / causality / Counterfactuals / Datenbanken / Datenmanagement / Statistics / TreatmentEffectEstimation / Wahrscheinlichkeitsrechnung und Statistik
ISBN:	9783031350504
EAN-Code:	9783031350504
Verlag:	Springer
Einband:	Gebunden
Sprache:	English
Dimensionen:	H 241 mm / B 160 mm / D 23 mm
Gewicht:	639 gr
Seiten:	316
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

This 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.