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Identifiability of intermediate variables on causal paths

Frontiers of Mathematics in China 2013年第3期8卷 517-539页

作者： Wanlu DENG Zhi GENG Peng LUO School of Mathematical Sciences Peking University Beijing 100871 China

We discuss the discovery of causal mechanisms and identifiability of intermediate variables on a causal path. Different from variable selection, we try to distinguish intermediate variables on the causal path from other variables. It is also different from ordinary model selection approaches which do not concern the causal relationships and do not contain unobserved variables. We propose an approach for selecting a causal mechanism depicted by a directed acyclic graph （DAG） with an unobserved variable. We consider several causal networks, and discuss their identifiability by observed data. We show that causal mechanisms of linear structural equation models are not identifiable. Furthermore, we present that causal mechanisms of nonlinear models are identifiable, and we demonstrate the identifiability of causal mechanisms of quadratic equation models. Sensitivity analysis is conducted for the identifiability.

关键词： causal network directed acyclic graph (DAC) identifiability intermediate variable structural equation model

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A causal Model for Diagnostic Reasoning

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Journal of Computer Science & Technology 2000年第3期15卷 287-294页

作者：彭国强程虎 Institute of Software Chinese Academy of Sciences Beijing P.R. China

Up to now, there have been many methods for knowledge representation and reasoning in causal networks, but few of them include the research on the coactions of nodes. In practice, ignoring these coactions may influence the accuracy of reasoning and even give rise to incorrect reasoning. In this paper, based on multilayer causal networks, the definitions on coaction nodes are given to construct a new causal network called Coaction causal network, which serves to construct a model of neural network for diagnosis followed by fuzzy reasoning, and then the activation rules are given and neural computing methods are used to finish the diagnostic rea soning. These methods are proved in theory and a method of computing the number of solutions for the diagnostic reasoning is given. Finally, the experiments and the conclusions are presellted.

关键词： coaction node coaction causal network neural network fuzzy reasoning diagnostic reasoning causal network

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A hybrid approach of Knowledge Discovery in fishery resources evaluation

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西南师范大学学报(自然科学版) 1999年第5期24卷 536-544页

作者： Li\ Li Department of Computer Science,Southwest China Normal University,Chongqing 400715 西南师范大学计算机科学系重庆

A framework is put for ward,which integrates Knowledge Discovery in Database (KDD) with case based reasoning (CBR) and Bayesian *** following tasks are fulfilled:(1)discovering refined rules;(2)retrieving structured data which can not be described by flat vector feature;(3)forecasting the location of central fishing ground for the next *** these have been applied to a real case with some encouraging and interesting results.

关键词： knowledge discovery in databases bayesian network case based reasoning causal network

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Exploring the Big Data Using a Rigorous and Quantitative causality Analysis

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Journal of Computer and Communications 2016年第5期4卷 53-59页

作者： X. San Liang School of Marine Sciences and School of Mathematics Nanjing University of Information Science and Technology Nanjing China

causal analysis is a powerful tool to unravel the data complexity and hence provide clues to achieving, say, better platform design, efficient interoperability and service management, etc. Data science will surely benefit from the advancement in this field. Here we introduce into this community a recent finding in physics on causality and the subsequent rigorous and quantitative causality analysis. The resulting formula is concise in form, involving only the common statistics namely sample covariance. A corollary is that causation implies correlation, but not vice versa, resolving the long-standing philosophical debate over correlation versus causation. The applicability to big data analysis is validated with time series purportedly generated with hidden processes. As a demonstration, a preliminary application to the gross domestic product (GDP) data of United States, China, and Japan reveals some subtle USA-China-Japan relations in certain periods.

关键词： causality Big Data Information Flow Time Series causal network

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A Review of the Climate-Change-Impacts’Rates of Change in the Arctic

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Journal of Environmental Protection 2010年第1期1卷 59-69页

作者： Joseph Santhi Pechsiri Amir Sattari Paulina Garza Martinez Liu Xuan Department of Industrial Ecology Royal Institute of Technology(KTH)StockholmSweden Division of Indoor Environment Department of Technology and Built Environment KTH Research SchoolUniversity of GavleGavleSweden Department of Industrial Ecology Royal Institute of Technology(KTH)StockholmSweden

Climate Change is a global phenomenon that has a global scale impact. The current trend of climate change towards the warming of the globe has resulted in various changes in the geological, climatology, social, economical, and bio-logical processes worldwide. Temperature of the globe has increased due to various factors, but anthropogenic plays a major contribution through the heavy input of Greenhouse gases. One of the world’s most remote regions that have been affected by most of the anthropogenic stresses on environmental services is the Arctic Region. The Arctic Region has shown various drastic changes and has shown to be effected by various anthropogenic activities that take place elsewhere. These changes include the ozone hole (resulting from ozone degrading compound emitted heavily by an-thropogenic demands), the accumulation of various persistent and volatile pollutants (i.e. POPs), and the meltdown of the polar ice (among others). These drastic changes are well perceived and well projected for future preparations. However, the question still remains if these impacts would only accelerate change. This paper aims to discuss if these changes are accelerating or happening at a constant rate. In addition, this paper aims to only focus on changes due to global warming and climate changes

关键词： Climate Change Impacts Rates of Change causal network Arctic Ecosystems

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Fault Root Diagnosis based on Partial Symbol Transfer Entropy

Fault Root Diagnosis based on Partial Symbol Transfer Entrop...

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第40届中国控制会议

作者： Rui Chen Jian-Guo Wang Xue-Zhi Cai Yuan Yao Jian-Long Liu School of Mechatronical Engineering and Automation Shanghai UniversityShanghai Key Lab of Power Station Automation Technology Department of Chemical Engineering National Tsing-Hua University

Transfer entropy is a data-driven model-free method,which can obtain the causal relationship between variables,and has been applied in the fields of complex industrial process modeling,monitoring and fault *** entropy can detect the causal relationship between variables without assuming any basic *** the signal will appear non-stationary time series in which both mean and variance change,so the method of partial symbol transfer entropy is proposed to detect the causality of non-stationary time series,and then the causal network diagram is *** effectiveness of the method is verified by simulation and actual industrial case（Tennessee-Eastman process）.

关键词： Sequence transfer entropy causal network root cause of detection

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