Machine Learning and Causality
نویسندگان
چکیده
منابع مشابه
Machine learning approaches to statistical dependences and causality Dagstuhl Seminar
From 27.09.2009 to 02.10.2009, the Dagstuhl Seminar 09401 Machine learning approaches to statistical dependences and causality was held in Schloss Dagstuhl Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of sem...
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The Causality Workbench project is an environment to test causal discovery algorithms. Via a web portal (http://clopinet.com/causality), it provides a number of resources, including a repository of datasets, models, and software packages, and a virtual laboratory allowing users to benchmark causal discovery algorithms by performing virtual experiments to study artificial causal systems. We regu...
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The relationship between statistical dependency and causality lies at the heart of all statistical approaches to causal inference. Recent results in the ChaLearn cause-effect pair challenge have shown that causal directionality can be inferred with good accuracy also in Markov indistinguishable configurations thanks to data driven approaches. This paper proposes a supervised machine learning ap...
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Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem. Methods: In this...
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Predicting dust sources area and determining the affecting factors is necessary in order to prioritize management and practice deal with desertification due to wind erosion in arid areas. Therefore, this study aimed to evaluate the application of three machine learning models (including generalized linear model, artificial neural network, random forest) to predict the vulnerability of dust cent...
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ژورنال
عنوان ژورنال: IMF Working Papers
سال: 2019
ISSN: 1018-5941
DOI: 10.5089/9781513518305.001