Identifying the Causal Effects of Stochastic Interventions by Augmented Causal Networks

Suppose that the cause-effect relationships between variables can be described by a causal network. To identify the causal effect of a stochastic intervention, an augmented causal network for stochastic intervention is proposed in this paper. Then, we obtain two graphical criteria for identifying the causal effects of stochastic interventions from passive observations on observed variables only. When either of the two criteria is satisfied, a simple closed-form expression is provided for the causal effect of a stochastic intervention, which enables researchers to assess the causal effect with little effort.     

Relationship of causal effects in a causal chain and related inference

This paper discusses the relationship among the total causal effect and local causal effects in a causal chain and identifiability of causal effects. We show a transmission relationship of causal effects in a causal chain. According to the relationship, we give an approach to eliminating confounding bias through controlling for intermediate variables in a causal chain.     

Learning dynamic causal relationships among sugar prices

In this paper, we are interested in exploring the dynamic causal relationships among two sets of three variables in different quarters. One set is futures sugar closing price in Zhengzhou futures exchange market (ZC), spot sugar price in Zhengzhou (ZS) and futures sugar closing price in New York futures exchange market(NC) and the other includes futures sugar opening price in Zhengzhou (ZO), ZS and NC. For each quarter, we first use Bayesian model selection to obtain the optimal causal graph with the highest BD scores and then use Bayesian model averaging approach to explore the causal relationship between every two variables. From the real data analysis, the two conclusions almost coincide, which shows that the two methods are practical.     

ABC-Miner+: constructing Markov blanket classifiers with ant colony algorithms

ABC-Miner is a Bayesian classification algorithm based on the Ant colony optimization (ACO) meta-heuristic. The algorithm learns Bayesian network Augmented Naïve-Bayes (BAN) classifiers, where the class node is the parent of all the nodes representing the input variables. However, this assumes the existence of a dependency relationship between the class variable and all the input variables, and this relationship is always a type of “causal” (rather than “effect”) relationship, which restricts the flexibility of the algorithm to learn. In this paper, we extended the ABC-Miner algorithm to be able to learn the Markov blanket of the class variable. Such a produced model has a more flexible Bayesian network classifier structure, where it is not necessary to have a (direct) dependency relationship between the class variable and each of the input variables, and the dependency between the class and the input variables varies from “causal” to “effect” relationships. In this context, we propose two algorithms: \({\hbox {ABC-Miner}+_1}\) , in which the dependency relationships between the class and the input variables are defined in a separate phase before the dependency relationships among the input variables are defined, and \({\hbox {ABC-Miner}+_2}\) , in which the two types of dependency relationships in the Markov blanket classifier are discovered in a single integrated process. Empirical evaluations on 33 UCI benchmark datasets show that our extended algorithms outperform the original version in terms of predictive accuracy, model size and computational time. Moreover, they have shown a very competitive performance against other well-known classification algorithms in the literature.     

Is there a causal relation between mathematical creativity and mathematical problem-solving performance?

The relationship between mathematical creativity (MC) and mathematical problem-solving performance (MP) has often been studied but the causal relation between these two constructs has yet to be clearly reported. The main purpose of this study was to define the causal relationship between MC and MP. Data from a representative sample of 480 eighth-grade students were analysed using a cross-lagged panel correlation (CLPC) design. CLPC attempts to rule out plausible alternative explanation of a causal effect. The result suggests that significant predominant causal relationship was found between MC and MP. It indicates that MP was found to be a cause of MC than the converse.     

基于VAR和DAG的中国经济增长实证研究

本文利用近年来新发展的DAG方法对我国的GDP、投资、消费和出口的因果关系进行研究.与以前的研究方法相比较,DAG方法可为宏观经济变量的结构VAR模型的过度识别提供限制,同时能给出经济变量的同期和动态因果关系.实证研究表明,投资和消费既是我国GDP增长的同期原因,又是经济增长的短期和长期原因,而且实证结论不支持我国的出口导向型经济增长假设.     

中国人均碳排放影响因素的长期均衡与因果动态关系研究——基于结构突变ARDL-VECM模型的实证分析

为了探讨中国低碳经济的发展路径,运用BP内生结构突变点检验、考虑结构突变的ARDL模型和基于VECM模型的Granger因果关系检验方法,利用我国1985至2014年的相关数据,探讨经济增长、城镇化、技术创新、贸易开放与我国人均碳排放的长期均衡和短期动态关系,以及变量之间的因果关系。研究结果表明,中国人均碳排放与解释变量之间存在长期协整关系。长期来看,经济增长与人均碳排放之间呈倒U型关系,城市化水平的提高可以有效减少人均碳排放,技术创新与人均碳排放显著负相关,而贸易开放会引起环境恶化。短期来看,贸易开放与人均碳排放显著正相关,而其他变量对人均碳排放的影响均不显著。此外,基于VECM模型的Granger因果关系检验证实所有解释变量均为人均碳排放的Granger原因,且经济增长、贸易开放与人均碳排放之间存在双向因果关系。     

Identifiability of intermediate variables on causal paths

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.     

Prognostic of glial tumours: Presentation of the problem with solution

A study of glial tumours involving 192 cases is presented. Different issues are addressed: (i) the interrelationships between the histological variables, (ii) the problem of the prediction of the survival time, (iii) the causal role of the variables in the progress of the disease. We propose a three-level grade which can be defined alternatively with perivascular lymphocites or with the signs necrosis and neovascularization. We constructed a predictive model based on the Cox model in which the variables were chosen according to Akaike's criterion. In the explanatory analysis we dropped the variables which could be considered as consequences rather than causes of the disease and we first tested groups of variables (factors): we found that age, the topology and the histology of the tumour were explanatory.     

逆Weibull分布随机变量的随机比较

研究了两个相互独立的逆Weibull分布随机变量间的随机序,似然比序,危险率序以及凸序之间的相互关系,给出了两个相互独立但不同分布的随机变量满足各种随机序时其分布所含参数间的相应关系．也给出了两组相互独立但不同分布的随机变量极值间在一般随机序下的大小关系．     

逆Weibull分布随机变量的随机比较(英文)

研究了两个相互独立的逆Weibull分布随机变量间的随机序,似然比序,危险率序以及凸序之间的相互关系,给出了两个相互独立但不同分布的随机变量满足各种随机序时其分布所含参数间的相应关系.也给出了两组相互独立但不同分布的随机变量极值间在一般随机序下的大小关系.     

Connecting mathematical creativity to mathematical ability

This study aims to investigate whether there is a relationship between mathematical ability and mathematical creativity, and to examine the structure of this relationship. Furthermore, in order to validate the relationship between the two constructs, we will trace groups of students that differ across mathematical ability and investigate the relationships amongst these students’ performance on a mathematical ability test and the components of mathematical creativity. Data were collected by administering two tests, a mathematical ability and a mathematical creativity test, to 359 elementary school students. Mathematical ability was considered as a multidimensional construct, including quantitative ability (number sense and pre-algebraic reasoning), causal ability (examination of cause–effect relations), spatial ability (paper folding, perspective and spatial rotation abilities), qualitative ability (processing of similarity and difference relations) and inductive/deductive ability. Mathematical creativity was defined as a domain-specific characteristic, enabling individuals to be characterized by fluency, flexibility and originality in the domain of mathematics. The data analysis revealed that there is a positive correlation between mathematical creativity and mathematical ability. Moreover, confirmatory factor analysis suggested that mathematical creativity is a subcomponent of mathematical ability. Further, latent class analysis showed that three different categories of students can be identified varying in mathematical ability. These groups of students varying in mathematical ability also reflected three categories of students varying in mathematical creativity.     

结构VAR的有向非循环图模型

研究用图模型方法辨识结构向量自回归(VAR)模型,图中的结点表示不同时刻的随机变量,结点间的边表示其所表示的随机变量之间存在的因果相依关系.针对建立有向非循环图的问题,提出了一种基于回归分析的判断方法,用回归方程的回归平方和之差作为统计量,确定当前变量之间相依关系的方向.与R ea le的逐一判别法和A lessio的图搜索方法相比,文中提出的基于统计分析的方法简单易行,且可获得唯一的当前变量有向非循环图.最后以两组模拟序列为例,验证了所提出的方法是可行且有效的.     

Ⅰ型极值分布随机变量的随机比较(英文)

研究了两个相互独立的Ⅰ型极大值分布随机变量间的随机序,似然比序,危险率序及凸序之间的相互关系,给出了两个相互独立但不同分布的随机变量满足各种随机序时其分布所含参数间的相应关系.文中也给出了两组相互独立但不同分布的随机变量极值间在一般随机序下的大小关系.     

Ⅰ型极值分布随机变量的随机比较

研究了两个相互独立的Ⅰ型极大值分布随机变量间的随机序,似然比序,危险率序及凸序之间的相互关系,给出了两个相互独立但不同分布的随机变量满足各种随机序时其分布所含参数间的相应关系．文中也给出了两组相互独立但不同分布的随机变量极值间在一般随机序下的大小关系．     

Causal statistical inference in high dimensions

We present a short selective review of causal inference from observational data, with a particular emphasis on the high-dimensional scenario where the number of measured variables may be much larger than sample size. Despite major identifiability problems, making causal inference from observational data very ill-posed, we outline a methodology providing useful bounds for causal effects. Furthermore, we discuss open problems in optimization, non-linear estimation and for assigning statistical measures of uncertainty, and we illustrate the benefits and limitations of high-dimensional causal inference for biological applications.     

The Algebra of Estimation in Linear Econometric Systems∗

In econometrics it is common for variables to be related together in a set of linear, multilateral and causal interdependencies. This type of system generally has properties which are unsatisfactory for application of classical regression techniques. Consequently, alternative estimation methods have been developed. This paper explores the relations between several such methods in terms of symmetric idempotents of predetermined variables and their orthogonal complements. Generalizations of two‐ and three‐stage least squares and instrumental variables are considered, including Wicken's estimator.² The relative efficiencies of the estimators are also discussed.

     

SHFE与LME期铜价格关系实证研究

本文介绍了Granger引导关系模型,并利用这个模型对伦敦金属交易所(LME)三个月期铜和上海期货交易所(SHFE)五个月期铜进行了价格引导关系检验。检验结果显示,伦敦金属交易所三个月期铜价格滞后引导上海期货交易所五个月期铜价格,但是上海期货交易所对伦敦金属交易所的期铜价格不具有滞后价格引导关系。     

Stress testing network reconstruction via graphical causal model

An resilience optimal evaluation of financial portfolios implies having plausible hypotheses about the multiple interconnections between the macroeconomic variables and the risk parameters. In this article, we propose a graphical model for the reconstruction of the causal structure that links the multiple macroeconomic variables and the assessed risk parameters, it is this structure that we call stress testing network. In this model, the relationships between the macroeconomic variables and the risk parameter define a “relational graph” among their time‐series, where related time‐series are connected by an edge. Our proposal is based on the temporal causal models, but unlike, we incorporate specific conditions in the structure which correspond to intrinsic characteristics this type of networks. Using the proposed model and given the high‐dimensional nature of the problem, we used regularization methods to efficiently detect causality in the time‐series and reconstruct the underlying causal structure. In addition, we illustrate the use of model in credit risk data of a portfolio. Finally, we discuss its uses and practical benefits in stress testing.     

我国人口城市化与房地产发展协调研究

分析了我国人口城市化与房地产市场的关系,利用相关分析、因果分析、脉冲和方差分析,发现人口城市化率短期内受房地产市场影响较大,长期内房地产市场要受人口城市化速度的影响;研究了人口系统与房地产系统的熵流关系,利用静态和动态协调度定量分析,发现两个系统取得了较好协调发展效果,但未来协同效应存在下降的可能.