论键合图贮能元件的因果关系对系统动力学方程结构的影响

较详细地讨论了系统键合图贮能元件不同因果关系所对应的系统动力学正、逆问题方程的结构 ,这对系统动力学正、逆问题的计算机辅助建模及分析很有益处     

城市化水平与房地产投资增长率的关系研究

城市化水平是衡量一个国家经济发展程度的重要标志,人口比例指标是最常用的城市化测度指标。随着城市化进程的加快,必然带来房地产投资的增加。文中利用格兰杰因果分析法（Granger Test of Causality）,对我国1986-2003年间城市化进程和房地产投资增长作了实证研究。研究表明,目前我国房地产投资和城市化水平都处于不断上升阶段,房地产投资增长速度快于城市化增长,两者均保持相对平稳上升的趋势,且具有一定相关性,相关系数为0.205,具有相互促进的因果关系。     

Ethics,science, and the mechanisation of the world picture

A nascent science in the sixteenth century rejected explanations in terms of purpose in favour of causality, and this bias has persisted and grown stronger. It has unfortunate consequences in areas where social and ethical considerations should prevail, and the paper describes a search extending over 20 years for a way in which these consequences could be avoided.

房地产投资与地区经济增长的关系—基于1997-2007年分省数据检验

本文基于房地产投资角度, 采用1997-2007年分省数据, 利用协整检验和格兰杰因果检验方法, 实证检验房地产投资与地区经济增长的关系。研究结果显示, 房地产投资与地区经济增长之间存在长期关系, 房地产投资可以促进地区经济增长, 地区经济增长也可以促进房地产投资。     

Counterfactual inference to predict causal knowledge graph for relational transfer learning by assimilating expert knowledge --Relational feature transfer learning algorithm

Transfer learning (TL) is a machine learning (ML) method in which knowledge is transferred from the existing models of related problems to the model for solving the problem at hand. Relational TL enables the ML models to transfer the relationship networks from one domain to another. However, it has two critical issues. One is determining the proper way of extracting and expressing relationships among data features in the source domain such that the relationships can be transferred to the target domain. The other is how to do the transfer procedure. Knowledge graphs (KGs) are knowledge bases that use data and logic to graph-structured information; they are helpful tools for dealing with the first issue. The proposed relational feature transfer learning algorithm (RF-TL) embodies an extended structural equation modelling (SEM) as a method for constructing KGs. Additionally, in fields such as medicine, economics, and law related to people’s lives and property safety and security, the knowledge of domain experts is a gold standard. This paper introduces the causal analysis and counterfactual inference in the TL domain that directs the transfer procedure. Different from traditional feature-based TL algorithms like transfer component analysis (TCA) and CORelation Alignment (CORAL), RF-TL not only considers relations between feature items but also utilizes causality knowledge, enabling it to perform well in practical cases. The algorithm was tested on two different healthcare-related datasets — sleep apnea questionnaire study data and COVID-19 case data on ICU admission — and compared its performance with TCA and CORAL. The experimental results show that RF-TL can generate better transferred models that give more accurate predictions with fewer input features.     

Oil production cost,financial development,and economic growth in Russia

The study investigates the causal relationship between fossil energy sources, the production cost of oil and financial development on economic growth in Russia. The results show that Russian companies’ production cost of oil and oil prices cause economic growth and the one-way causality is negative. We also find that there is one-way positive causality from natural gas price, financial development, and education to economic growth. The negative oil price effect supports the resource curse hypothesis, whereas the positive natural gas price effect does not. Russian policies should focus on lowering companies’ production cost of oil, improving financial development and investing in education.     

Simplified Granger causality map for data-driven root cause diagnosis of process disturbances

Root cause diagnosis is an important step in process monitoring, which aims to identify the sources of process disturbances. The primary challenge is that process disturbances propagate between different operating units because of the flow of material and information. Data-driven causality analysis techniques, such as Granger causality (GC) test, have been widely adopted to construct process causal maps for root cause diagnosis. However, the generated causal map is over-complicated and difficult to interpret because of the existence of process loops and the violation of statistical assumptions. In this work, a two-step procedure is proposed to solve this problem. First, a causal map is built by adopting the conditional GC analysis, which is viewed as a graph in the next step. In this graph, each vertex corresponds to a process variable under investigation, while the weight of the edge connecting two vertices is the F-value calculated by conditional GC. This graph is then simplified by computing its maximum spanning tree. Thus, the results of the causality analysis are transformed into a directed acyclic graph, which eliminates all loops, highlights the root cause variable, and facilitates the diagnosis. The feasibility of this method is illustrated with the application to the Tennessee Eastman benchmark process. In the investigated case studies, the proposed method outperforms the conditional GC test and provides an easy way to identify the root cause of process disturbances.     

CAUSALITY AND SOCIAL SYSTEMS ANALYSIS

In the study of social systems a causal orientation leads to the familiar two-variable analysis of components of the systems, rather than the analysis of the characteristics of the system as a whole. The result is that predictions made about social systems are usually either wrong or trivially correct. A general systems approach does not lean on causal analysis and, therefore, provides the analytical framework for comprehending system characteristics, and predicting their state.     

Oil prices and financial stress: A volatility spillover analysis

This paper examines whether there is a volatility transmission between oil prices and financial stress by means of the volatility spillover test. We employ WTI crude oil prices and Cleveland financial stress index for the period 1991–2014 and divide the sample into pre-crisis, in-crisis, and post-crisis periods due to the downward trend in oil price in 2008. The volatility model estimations indicate that oil prices and financial stress index are dominated by long-run volatility. The volatility spillover causality test supports evidence on risk transfer from oil prices to financial stress before the crisis and from financial stress to oil prices after the crisis. The impulse response analysis shows that the volatility transmission pattern has similar dynamics before and after the crisis and is characterized by higher and long-lived effects during the crisis. Our results have implications for both policy makers and investors, and for future work.     

Cognitive engineering practice: melting theory into reality

Given the growing scope of cognitive engineering, our intent in this paper is to help uncover some of the regularities of the discipline. Adopting a pragmatic view, we attempt to interpret what cognitive engineers really do in practice, and to partly systematize this reality. The paper stresses the need for: (i) an explicit consideration of the demand for intervention and the analyst's preconceptions during the framing of the world-to-study, (ii) the adoption of multiple views for a sufficient understanding of the analysed world, (iii) a continuous reframing of the system under consideration (i.e., topological boundaries as well as timeframes) as the understanding of reality unfolds, (iv) acceptance and exploitation of the dialectic process between analysis/understanding and design/prediction.