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Titlebook: Cause Effect Pairs in Machine Learning; Isabelle Guyon,Alexander Statnikov,Berna Bakir Bat Book 2019 Springer Nature Switzerland AG 2019 C

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发表于 2025-3-21 17:27:32 | 显示全部楼层 |阅读模式
书目名称Cause Effect Pairs in Machine Learning
编辑Isabelle Guyon,Alexander Statnikov,Berna Bakir Bat
视频videohttp://file.papertrans.cn/223/222644/222644.mp4
概述Comprehensive reference for those interested in the cause-effect problem, and how to tackle them using machine learning algorithms.Includes six tutorial chapters, beginning with the simplest cases and
丛书名称The Springer Series on Challenges in Machine Learning
图书封面Titlebook: Cause Effect Pairs in Machine Learning;  Isabelle Guyon,Alexander Statnikov,Berna Bakir Bat Book 2019 Springer Nature Switzerland AG 2019 C
描述This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms.  Based on the results of the .ChaLearn Cause-Effect Pairs Challenge., this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a “causal mechanism”, in the sense that the values of one variable may have been generated from the values of the other.  .This book provides both tutorial material on the state-of-the-art on cause-effect pairs and exposes the reader to more advanced material, with a collection of selected papers. Supplemental material includes videos, slides, and code which can be found on the workshop website..Discovering causal relationships from observational data will become increasingly important in data science with the increasing amount of available data, as a means of detecting potential triggers in epidemiology, social sciences, economy, biology, medicine, and other sciences.
出版日期Book 2019
关键词Causality; cause-effect pairs; large scale design; causal direction; causal inference; causality in machi
版次1
doihttps://doi.org/10.1007/978-3-030-21810-2
isbn_softcover978-3-030-21812-6
isbn_ebook978-3-030-21810-2Series ISSN 2520-131X Series E-ISSN 2520-1328
issn_series 2520-131X
copyrightSpringer Nature Switzerland AG 2019
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Apotheke 2010: Apothekenformate mit Zukunft in the inference of causal-effect relationships. We also study the combination of the proposed measures with standard statistical measures in the framework of the ChaLearn cause-effect pair challenge. The developed model obtains an AUC score of 0.82 on the final test database and ranked second in the challenge.
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Ostdeutsche Verwaltungskultur im Wandel. and . → .. In this chapter, we first define what is meant by generative modeling and what are the main assumptions usually invoked in the literature in this bivariate setting. Then we present the theoretical identifiability problem that arises when considering causal graph with only two variables.
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Ost- und westdeutsche Spracheinstellungen to then ask how such methods could generalize beyond the two variable case to settings that either involve more variables—such as is the case in graph learning—or to settings where the relationship between the candidate variables does not fall into one of the classes defined by the challenges. This
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Mit Broiler gegen Wessi-Hochmutels contaminated with additive non-Gaussian noise. Assuming that the causes and the effects have the same distribution, we show that the distribution of the residuals of a linear fit in the anti-causal direction is closer to a Gaussian than the distribution of the residuals in the causal direction.
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