Extracting informative variables in the validation of two-group causal relationship

The validation of causal relationship between two groups of multivariate time series data often requires the precedence knowledge of all variables. However, in practice one finds that some variables may be negligible in describing the underlying causal structure. In this article we provide an explicit definition of “non-informative variables” in a two-group causal relationship and introduce various automatic computer-search algorithms that can be utilized to extract informative variables based on a hypothesis testing procedure. The result allows us to represent a simplified causal relationship by using minimum possible information on two groups of variables.     

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 。      

On Data-Driven Computation of Information Transfer for Causal Inference in Discrete-Time Dynamical Systems

In this paper, we provide a novel approach to capture causal interaction in a dynamical system from time series data. In Sinha and Vaidya (in: IEEE conference on decision and control, pp 7329–7334, 2016), we have shown that the existing measures of information transfer, namely directed information, Granger causality and transfer entropy, fail to capture the causal interaction in a dynamical system and proposed a new definition of information transfer that captures direct causal interactions. The novelty of the information transfer definition used in this paper is the fact that it can differentiate between direct and indirect influences Sinha and Vaidya (2016). The main contribution of this paper is to show that the proposed definition of information transfers in Sinha and Vaidya (2016) and Sinha and Vaidya (in: Indian control conference, pp 303–308, 2017) can be computed from time series data, and thus, the direct influences in a dynamical system can be identified from time series data. We use transfer operator theoretic framework, involving Perron–Frobenius and Koopman operators for the data-driven approximation of the system dynamics and computation of information transfer. Several examples, involving linear and nonlinear system dynamics, are presented to verify the efficiency of the developed algorithm.

     

Qualitative operators for reasoning maps: Evaluating multi-criteria options with networks of reasons

Cognitive/causal maps have been widely used as a powerful way of capturing decision-makers’ views about a problem, representing it as a cause–effect discourse. Several ways of making causal inferences from this type of model have been proposed in the Operational Research and Artificial Intelligence literatures, but none, as far as we are aware, has attempted to use a causal map structure to perform a multi-criteria evaluation of decision alternatives. Recently, we have proposed a new multi-criteria method, denominated as a Reasoning Map, which permits the use of decision-makers’ reasoning, structured as a network of means-and-ends (a particular type of causal map) to perform such an evaluation. In this manner, the model resembles the way that people talk and think about decisions in practice. The method also pays explicit attention to the cognitive limitations of decision-makers in providing preference information. Thus it employs qualitative assessment of preferences, utilises aggregation operators for qualitative data and provides also qualitative outputs. In this paper we discuss and evaluate possible ways of aggregating qualitative performance information in Reasoning Maps.     

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.     

Explaining modal intuition

The paper defends causal explanationism concerning our modal intuitions and judgments, and, in particular, the following claims. If a causally explainable mirroring or “pre-established harmony” between our mind and modal reality obtains, we are justified in believing it does. We do not hold our modal beliefs compulsively and blindly but with full subjective and objective justification. Therefore, causal explanation of our modal beliefs does not undermine rational trust in them. Explanation and trust support each other. In contrast, anti-explanationists (from Kant, through neo-wittgensteinians to T. Nagel and J. Pust), claim that causal explanation of intuitions and judgments undermines rational trust in them. They especially target causal explanation in terms of pre-established harmony between our mind, shaped by causal processes, and the underlying modal structure of reality. The paper argues against them. The argument builds upon the claim that the appeal to modal facts is indispensable for systematization and explanation of non-modal ones. Therefore, we should assume that modal facts exist and are not disjoint and isolated from actual facts. The modal structure of the universe intervenes in the non-modal reality. Causal processes indirectly carry information about deep modal structure. Any (reasonable candidate) causal explanation of our intuitional modal beliefs should start from this indirect contact with and information about modal facts. Therefore, if our intuitional modal beliefs are true and causally explainable (by a factual, non-modal explanans), they are true in virtue of the deep underlying modal structure. They are sensitive to modal reality and track it. We can come to know this fact, and thus strengthen our spontaneous trust in our modal intuitions.     

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.     

Coupling vs. conductance for the Jerrum–Sinclair chain*

We address the following question: is the causal coupling method as strong as the conductance method in showing rapid mixing of Markov chains? A causal coupling is a coupling which uses only past and present information, but not information about the future. We answer the above question in the negative by showing that there exists a bipartite graph G such that any causal coupling argument on the Jerrum–Sinclair Markov chain for sampling almost uniformly from the set of perfect and near perfect matchings of G must necessarily take time exponential in the number of vertices in G. In contrast, the above Markov chain on G has been shown to mix in polynomial time using conductance arguments. © 2001 John Wiley & Sons, Inc. Random Struct. Alg., 18: 1–17, 2001     

Fractality and self‐organization in the orthodox iconography

The authors consider the Orthodox iconography of Byzantine style aimed at examining the existence of complex behavior and fractal patterns. It has been demonstrated that fractality in icons is manifested as two types—descending and ascending, where the former one corresponds to the apparent information and the latter one to the hidden causal information defining the spatiality of icon. Self‐organization, recognized as the increase of the causal information in temporal domain, corresponds to contextualization of the observer's personage position. The results presented in the forms of plots and tables confirm the adequacy of the model being the completion of visual perception. © 2015 Wiley Periodicals, Inc. Complexity 21: 55–68, 2016     

Dependence of inter-neuronal effective connectivity on synchrony dynamics in neuronal network motifs

Effective connectivity, characterized as directional causal influences among neural units, is functionally significant to be reconstructed. Various dynamic regimes have been considered to underlie reshaping of the effective connections. In this work, the impact of zero-lag synchronization on the reconstruction of effective connectivity in neuronal network motifs is investigated. The synchronization analysis and effective connectivity estimation by using Granger causality (GC) method are performed. It is shown that the synchronization of the neurons at zero lag contributes to the reconstruction of reciprocal effective connections without synaptic connections. In addition, delay-induced zero-lag synchronous transition facilitates dynamic transformation of the causal interactions. With the increase of synaptic coupling strength, the causal interplay undergoes the transition to be statistically significant at a critical value. Furthermore, it can be found that multiple effective motifs are extracted from different synchronization states of the underlying structural motifs. GC measures of effective connectivity are proved to be reliable compared with the Information Flow for causal analysis. The obtained results may be helpful to future research about information processes.     

Program explanations and causal relevance

Frank Jackson and Philip Pettit have defended a non-reductive account of causal relevance known as the ‘program explanation account’. Allegedly, irreducible mental properties can be causally relevant in virtue of figuring in non-redundant program explanations which convey information not conveyed by explanations in terms of the physical properties that actually do the ‘causal work’. I argue that none of the possible ways to spell out the intuitively plausible idea of a program explanation serves its purpose, viz., defends non-reductive physicalism against Jaegwon Kim’s Causal Exclusion Argument according to which non-reductive physicalism is committed to epiphenomenalism because irreducible mental properties are ‘screened off’ from causal relevance by their physical realizers. Jackson and Pettit’s most promising explication of a program explanation appeals to the idea of invariance of effect under variation of realization, but I show that invariance of effect under variation of realization is neither necessary nor sufficient for causal relevance.     

Causal optimal transport and its links to enlargement of filtrations and continuous-time stochastic optimization

The martingale part in the semimartingale decomposition of a Brownian motion with respect to an enlargement of its filtration, is an anticipative mapping of the given Brownian motion. In analogy to optimal transport theory, we define causal transport plans in the context of enlargement of filtrations, as the Kantorovich counterparts of the aforementioned non-adapted mappings. We provide a necessary and sufficient condition for a Brownian motion to remain a semimartingale in an enlarged filtration, in terms of certain minimization problems over sets of causal transport plans. The latter are also used in order to give robust transport-based estimates for the value of having additional information, as well as model sensitivity with respect to the reference measure, for the classical stochastic optimization problems of utility maximization and optimal stopping.     

Extending and interpreting Post’s programme

Computability theory concerns information with a causal-typically algorithmic-structure. As such, it provides a schematic analysis of many naturally occurring situations. Emil Post was the first to focus on the close relationship between information, coded as real numbers, and its algorithmic infrastructure. Having characterised the close connection between the quantifier type of a real and the Turing jump operation, he looked for more subtle ways in which information entails a particular causal context. Specifically, he wanted to find simple relations on reals which produced richness of local computability-theoretic structure. To this extent, he was not just interested in causal structure as an abstraction, but in the way in which this structure emerges in natural contexts. Post’s programme was the genesis of a more far reaching research project.In this article we will firstly review the history of Post’s programme, and look at two interesting developments of Post’s approach. The first of these developments concerns the extension of the core programme, initially restricted to the Turing structure of the computably enumerable sets of natural numbers, to the Ershov hierarchy of sets. The second looks at how new types of information coming from the recent growth of research into randomness, and the revealing of unexpected new computability-theoretic infrastructure. We will conclude by viewing Post’s programme from a more general perspective. We will look at how algorithmic structure does not just emerge mathematically from information, but how that emergent structure can model the emergence of very basic aspects of the real world.     

On the changing structure among Chinese equity markets: Hong Kong,Shanghai, and Shenzhen

This study investigates information discovery among five Chinese equity markets measured daily over the period 1995–2014. We employ time series methods for finding structural breaks (if any) and uncovering both short-run and long-run fluctuations. We apply a new algorithm of inductive causation for use with non-Gaussian data to study the information flows in contemporaneous time. The empirical results show that there are four break dates and that the underlying causal models changed over our study period. The Shanghai A-share market dominates the other markets in the most recent period.     

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.     

A systems approach to identifying decisive information for sustainable development

Many governments are striving to implement sustainable development programs. While there are many definitions of `sustainability', most agree that a more comprehensive information infrastructure including economic, social, environmental, and cultural measures is required to assess courses of action and evaluate progress. Also critical is the development of information about the structure and behavior of the systems in which decisions are made. Most of the efforts toward the identification of information to support sustainable development decision making have focused on developing measures of progress toward sustainability. The Pressure-State-Response framework has been suggested as a method for capturing perceptions of causality. This framework fails to capture important information about complex causal relationships and system behavior. A systems approach to identifying decisive information is discussed as an alternative. This approach supports the identification of relationships among the indicators, learning about the behavior of the system, and provides a common language for interdisciplinary communication.     

Comments on “Lyapunov and external stability of Caputo fractional order switching systems”

The stability of Caputo fractional order switching systems is studied in the article by Wu C. etc (Wu and Liu (2019)). The authors claim that the lower bound of the Caputo fractional order derivative needs to be updated at each switching instant. However, the lower bound is relevant to the initial condition and reflects the historical information of a fractional system. No historical information can be changed by subsequent control input as all physical systems are causal systems. The model in Wu and Liu (2019) is physically unattainable and the theoretical achievements cannot be applied in engineering.     

A Bayesian network approach to making inferences in causal maps

The main goal of this paper is to describe a new graphical structure called ‘Bayesian causal maps’ to represent and analyze domain knowledge of experts. A Bayesian causal map is a causal map, i.e., a network-based representation of an expert’s cognition. It is also a Bayesian network, i.e., a graphical representation of an expert’s knowledge based on probability theory. Bayesian causal maps enhance the capabilities of causal maps in many ways. We describe how the textual analysis procedure for constructing causal maps can be modified to construct Bayesian causal maps, and we illustrate it using a causal map of a marketing expert in the context of a product development decision.     

基于MASIM的社会推理与计算系统

随着计算机和信息技术的发展,信息科学技术的研究越来越重视与社会科学的交叉,社会计算已成为国内外计算机及相关领域的最新研究热点.社会计算与社会智能研究的核心之一是社会因果关系推理和行为评判问题.基于认知和心理学理论,介绍一个社会推理计算模型MASIM以及基于MASIM的社会计算系统实例,并以此阐述建立社会推理机制和社会计算系统的若干技术方面.