Gauss因果模型中因果效应识别方法的比较

一个Gauss因果模型中常常存在不只一种识别因果效应的方法, 不同的方法对应的估计可能不同. 该文对Pearl等人提出的前门准则, 后门准则,工具变量准则等识别方法的估计精度进行了分析比较, 并给出了相应的模拟结果, 为实践中选择更优的识别准则提供了依据.     

多维时间序列Granger因果性的一种图模型学习方法

传统的两变量Granger因果分析法容易产生伪因果关系,且不能刻画变量间的同期因果性.利用图模型方法研究多维时间序列变量间Granger因果关系,通过Granger因果图的建立将问题转化为Granger因果图结构的辨识问题,利用局部密度估计法构造相应的辨识统计量,采用bootstrap方法来确定检验统计量的原分布.模拟分析以及对于中国股市间Granger因果关系的研究说明了该方法的有效性.     

因果链上因果效应的关系及推断

探讨因果链上总因果效应与局部因果效应的关系及因果效应的可识别性. 给出了在因果链上因果效应的传递关系. 根据这个关系, 给出了通过控制因果链上中间因素消除混杂偏倚的方法.     

基于信息传递解释的σ干预及其因果演算

因果图模型框架和潜在结果框架是当今因果建模的两个主要框架,有其各自的特点和相对流行的应用领域.一个结合两个框架优点的工作是单世界干预图模型(SWIG)和基于它的po-演算.本文受到因果关系的信息传递解释的启发,提出了信息干预算子σ(·)和信息因果网络,给出了相应的σ-演算.一方面,指出了它和do-演算的联系和区别;另一方面,它与SWIG一样具备判断观测变量和反事实变量之间条件独立性的便利性,同时还有类似do的形式化σ算子.     

基于多变量的Granger因果检验方法

针对既有的Granger因果检验方法只能处理两个变量间的因果关系问题,指出这种方法会导致间接因果与直接因果的混淆及由于数据同源而产生的伪因果问题,提出一种可以消除以上问题的多变量因果检验方法,该方法立足于原有的Granger因果检验,适用于短时相依的和平稳的时间序列数据,并根据蒙特卡罗方法给出了统计推断的检验量,设计了方法的实施步骤.最后,应用一个仿真的实例具体展示了方法的使用过程和方法的有效性。     

关于计时事件图的同步性

计时事件图可用于通信、制造等系统的建模、分析、控制和优化.运用极大加代数方法研究计时事件图的同步性.对自治系统,在极大子系统之间添加时间限制来得到系统的同步,并给出同步系统的一些性质和系统同步的最短周期时间.对非自治系统,通过因果反馈得到系统的同步性,并给出较短的周期时间.     

Granger因果检验的非线性进展及应用研究

Granger因果检验是计量经济学的重要组成部分,也是现代经济、金融学分析的重要工具,近年来Granger因果检验在非线性检验方向有了较大进展。本文在线性Granger因果检验的基础上,阐述了Granger因果检验的非线性进展,重点总结了针对一阶矩的基于回归模型、非参函数和信息理论的三大类非线性方法以及针对二阶矩的基于残差交叉相关系数和多元条件方差模型下的两大类非线性方法,讨论了不同非线性Granger方法中数据要求、核心模型、建模关键以及模型优缺点,提出了Granger因果检验"线性-非线性"的整体框架和研究范式.通过模型分析和比较,本文可为因果检验的非线性理论和模型研究提供参考,并对因果检验在经济和金融领域的更广泛应用提供支持。     

占座效应的数学建模与统计分析

利用因果图模型及其可识别性理论对占座效应 ,即占座行为对座位使用率的影响进行了建模 .并利用这一模型对实际例子中的占座效应进行了分析 ,最终得出占座行为对实例中座位使用率的影响程度     

因果模型中因果效应的可识别性研究

对有向非循环图中的因果关系进行探讨,提出了相应的虚拟事实模型,并利用条件独立关系作为辅助信息对一种较为简单的虚拟事实模型的可识别性进行了研究.将可忽略假设推广为可替换性假设,并且得到：在可替换性假设之下,因果效应具有可识别性,即有可能从观察数据直接计算因果效应.     

非线性结构向量自回归因果图的广义似然比辨识方法

利用图模型方法研究非线性结构向量自回归模型的因果性问题.构建了非线性结构向量自回归因果图模型,提出图模型因果性的广义似然比辨识方法.构造同期因果关系和滞后因果关系的广义似然比统计量,使用bootstrap方法来确定检验统计量的原分布,模拟研究论述了方法的有效性.     

Identifiability of causal effect for a simple causal model

Counterfactual model is put forward to discuss the causal inference in the directed acyclic graph and its corresponding identifiability is thus studied with the ancillary information based on conditional independence. It is shown that the assumption of ignorability can be expanded to the assumption of replaceability,under which the causal efiects are identifiable.     

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.     

On Flat Complete Causal Lorentzian Manifolds

We describe up to finite coverings causal flat affine complete Lorentzian manifolds such that the past and the future of any point are closed near this point. We say that these manifolds are strictly causal. In particular, we prove that their fundamental groups are virtually abelian. In dimension 4, there is only one, up to a scaling factor, strictly causal manifold which is not globally hyperbolic. For a generic point of this manifold, either the past or the future is not closed and contains a lightlike straight line     

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.     

A weighted causal theory for acquiring and utilizing open knowledge

Motivated by enabling intelligent robots/agents to take advantage of open-source knowledge resources to solve open-ended tasks, a weighted causal theory is introduced as the formal basis for the development of these robots/agents. The action model of a robot/agent is specified as a causal theory following McCain and Turner's nonmonotonic causal theories. New knowledge is needed when the robot/agent is given a user task that cannot be accomplished only with the action model. This problem is cast as a variant of abduction, that is, to find the most suitable set of causal rules from open-source knowledge resources, so that a plan for accomplishing the task can be computed using the action model together with the acquired knowledge. The core part of our theory is constructed based on credulous reasoning and the complexity of corresponding abductive reasoning is analyzed. The entire theory is established by adding weights to hypothetical causal rules and using them to compare competing explanations which induce causal models satisfying the task. Moreover, we sketch a model theoretic semantics for the weighted causal theory and present an algorithm for computing a weighted-abductive explanation. An application of the techniques proposed in this paper is illustrated in an example on our service robot, KeJia, in which the robot tries to acquire proper knowledge from OMICS, a large-scale open-source knowledge resource, and solve new tasks with the knowledge.     

Boundary value problems for difference equations with causal operators

By using the monotone iterative method, some new results are established for nonlinear boundary conditions of difference problems with causal operators. We formulate sufficient conditions under which such problems have extremal solutions. Difference inequalities with causal operators are also discussed. Two examples are added to illustrate the results.     

产学研合作视角下企业技术获取协同及其与创新绩效的关系研究

企业根据自身特点选择技术合作伙伴,筛选技术来源,通过开展广泛的产学研合作实现多渠道技术获取是企业技术创新活动的重要组成部分,也是企业实现技术创新能力提升和经济效益增长的重要方式。不同技术获取模式对企业创新绩效的影响效果和作用机理差异性受到了诸多学者的重视,但企业技术获取系统是一个复合系统,很少有研究系统地分析企业技术获取系统的协同机理及其与创新绩效的互动关系。文章构建了企业技术获取系统协同发展模型,对我国企业技术获取的协同发展情况进行了实证分析,并进一步讨论了企业不同技术获取协同与不同创新绩效间的因果关系。研究表明:整体上我国企业技术获取系统处于协同演进状态,但国外技术获取子系统在发展过程中存在徘徊与波动;企业在技术获取和技术的消化吸收过程中形成了技术获取协同带动技术累计绩效和经济绩效刺激技术获取系统协同演进这两个简单的价值循联接;我国企业目前仍欠缺通过持续的技术学习实现技术累计绩效的商业化和市场化,使企业经济绩效归因于其内外部技术的研发、吸收和二次创新的能力。     

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.     

一类因果模型的可识别性条件

因果问题在近代医学,生物学,社会科学的研究中占有非常重要的地位。通过因果关系预见某些行为或策略对研究对象的影响已经成为一些实际研究的最终目的。Rubin(1978)提出了解决因果问题的虚拟事实模型,建立了因果推断统计分析的基本框架。虚拟事实模型的因果效应是以实际观测数据为研究对象的,但又不完全由数据之间的相关性决定,因此在讨论因果效应时存在可识别性问题。如果因果效应可识别,则有可能利用观测数据直接计算因果效应。但是,众 所周知:在不加任何假设或限制的条件下,虚拟事实模型的因果效应是不可识别的。若要研究变量间的因果效应就必须对虚拟事实模型加入某些必要的限制,使因果效应在这些限制下可识别。郑忠国,张艳艳,童行伟在“因果模型因果效应的可识别性研究”中针对控制变量与协变量相互独立的一类模型的可识别性进行了研究,指出在某些特定的可替换性假设之下,模型的因果效应具有可识别性。该文将针对控制变量作用于协变量的虚拟事实模型进行可识别性研究。作者将指出:控制变量是否作用于协变量并不影响因果效应的可识别性和可替换性假设。并给出:此类模型因果效应可唯一确定的充要条件 。      

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.