Definition Of Causality Research Articles

GRANGER CAUSALITY AND STRUCTURAL CAUSALITY IN CROSS-SECTION AND PANEL DATA

Granger noncausality in distribution is fundamentally a probabilistic conditional independence notion that can be applied not only to time series data but also to cross-section and panel data. In this paper, we provide a natural definition of structural causality in cross-section and panel data and forge a direct link between Granger (G–) causality and structural causality under a key conditional exogeneity assumption. To put it simply, when structural effects are well defined and identifiable,G–non-causality follows from structural noncausality, and with suitable conditions (e.g., separability or monotonicity), structural causality also impliesG–causality. This justifies using tests ofG–non-causality to test for structural noncausality under the key conditional exogeneity assumption for both cross-section and panel data. We pay special attention to heterogeneous populations, allowing both structural heterogeneity and distributional heterogeneity. Most of our results are obtained for the general case, without assuming linearity, monotonicity in observables or unobservables, or separability between observed and unobserved variables in the structural relations.

APPROPRIATE CAUSAL MODELS AND THE STABILITY OF CAUSATION

AbstractCausal models defined in terms of structural equations have proved to be quite a powerful way of representing knowledge regarding causality. However, a number of authors have given examples that seem to show that the Halpern–Pearl (HP) definition of causality (Halpern & Pearl, 2005) gives intuitively unreasonable answers. Here it is shown that, for each of these examples, we can give two stories consistent with the description in the example, such that intuitions regarding causality are quite different for each story. By adding additional variables, we can disambiguate the stories. Moreover, in the resulting causal models, the HP definition of causality gives the intuitively correct answer. It is also shown that, by adding extra variables, a modification to the original HP definition made to deal with an example of Hopkins & Pearl (2003) may not be necessary. Given how much can be done by adding extra variables, there might be a concern that the notion of causality is somewhat unstable. Can adding extra variables in a “conservative” way (i.e., maintaining all the relations between the variables in the original model) cause the answer to the question “Is X = x a cause of Y = y?” to alternate between “yes” and “no”? It is shown that we can have such alternation infinitely often, but if we take normality into consideration, we cannot. Indeed, under appropriate normality assumptions. Adding an extra variable can change the answer from “yes’ to “no”, but after that, it cannot change back to “yes”.

Concept of statistical causality and local martingales

In this paper we consider a statistical concept of causality in continuous time in filtered probability spaces which is based on Granger's definitions of causality. The given causality concept is closely connected to the preservation of the property being a local martingale if the filtration is getting larger. Namely, the local martingale remains unpredictable if the amount of information is increased. We proved that the preservation of this property is equivalent with the concept of causality.

COLLECTIVISM AND ECONOMIC DEVELOPMENT – A CAUSAL ANALYSIS

Does collectivism - contrary to Adam Smith's opinion - cause faster economic development? The paper argues in favour of the thesis that there is a positive, causal relation between the collectivist orientation of culture and economic development. I apply two approaches to support the hypothesis. First, I analyze the mechanisms relating the individualism/collectivism dimension of culture to economic growth. Second, I apply Wiener's definition of causality to the problem under consideration and estimate econometric models based on the example of EU member states.

Quantifying information transfer and mediation along causal pathways in complex systems

Measures of information transfer have become a popular approach to analyze interactions in complex systems such as the Earth or the human brain from measured time series. Recent work has focused on causal definitions of information transfer aimed at decompositions of predictive information about a target variable, while excluding effects of common drivers and indirect influences. While common drivers clearly constitute a spurious causality, the aim of the present article is to develop measures quantifying different notions of the strength of information transfer along indirect causal paths, based on first reconstructing the multivariate causal network. Another class of novel measures quantifies to what extent different intermediate processes on causal paths contribute to an interaction mechanism to determine pathways of causal information transfer. The proposed framework complements predictive decomposition schemes by focusing more on the interaction mechanism between multiple processes. A rigorous mathematical framework allows for a clear information-theoretic interpretation that can also be related to the underlying dynamics as proven for certain classes of processes. Generally, however, estimates of information transfer remain hard to interpret for nonlinearly intertwined complex systems. But if experiments or mathematical models are not available, then measuring pathways of information transfer within the causal dependency structure allows at least for an abstraction of the dynamics. The measures are illustrated on a climatological example to disentangle pathways of atmospheric flow over Europe.

THEORETICAL ASPECTS (DEFECTS) OF THE PREVENTION DIRECTION IN MEDICINE (CARDIOLOGY) AND THEIR APPLIED SIGNIFICANCE: CRITICS AND SUGGESTIONS

Prevention of the diseases, particularly cardial pathology, has its very significant controversies. The idea of the risk factors did not give the expected results. Causality of the majority of diseases is still unknown or not fully understood. This leads to inability of predicting. The article provides the reader with theoretical and practical experience evaluation of the XX century upon the problem of causality (etiology) of diseases. The original definition of causality is expressed and vindicated as its applied significance.

Shortcomings/Limitations of Blockwise Granger Causality and Advances of Blockwise New Causality.

Multivariate blockwise Granger causality (BGC) is used to reflect causal interactions among blocks of multivariate time series. In particular, spectral BGC and conditional spectral BGC are used to disclose blockwise causal flow among different brain areas in various frequencies. In this paper, we demonstrate that: 1) BGC in time domain may not necessarily disclose true causality and 2) due to the use of the transfer function or its inverse matrix and partial information of the multivariate linear regression model, both of spectral BGC and conditional spectral BGC have shortcomings and/or limitations, which may inevitably lead to misinterpretation. We then, in time and frequency domains, develop two new multivariate blockwise causality methods for the linear regression model called blockwise new causality (BNC) and spectral BNC, respectively. By several examples, we confirm that BNC measures are more reasonable and sensitive to reflect true causality or trend of true causality than BGC or conditional BGC. Finally, for electroencephalograph data from an epilepsy patient, we analyze event-related potential causality and demonstrate that both of the BGC and BNC methods show significant causality flow in frequency domain, but the spectral BNC method yields satisfactory and convincing results, which are consistent with an event-related time-frequency power spectrum activity. The spectral BGC method is shown to generate misleading results. Thus, we deeply believe that our new blockwise causality definitions as well as our previous NC definitions may have wide applications to reflect true causality among two blocks of time series or two univariate time series in economics, neuroscience, and engineering.

Hume über kausale Kräfte und Sätze

Abstract:This paper takes a new approach to understanding Hume’s view on causality, taking ontological as well as semantic issues into account. It attempts to attain three different goals. In the first place, it dismisses the main arguments that were put forward by the New Humeans in order to show that Hume must be regarded as a sceptical realist with respect to causality. Secondly, by employing a genetic reading of Hume’s two definitions of causality, the paper argues for an interpretation of Hume that takes his empirical criterion of meaning as seriously as possible. And thirdly, it presents a proposal to analyse the meaning of causal sentences from a Humean perspective. According to this approach, the meaning of causal sentences divides into a descriptive and a projective component. By taking advantage of Hume’s general view on meaning, we can underpin the projectivist interpretation of Hume’s theory of causality with exegetical evidence.

Cause, responsibility and blame: a structural-model approach

A definition of causality introduced by Halpern & Pearl, which uses structural equations, is reviewed. A more refined definition is then considered, which takes into account issues of normality and typicality, which are well known to affect causal ascriptions. Causality is typically an all-or-nothing notion: either A is a cause of B or it is not. An extension of the definition of causality to capture notions of degree of responsibility and degree of blame, due to Chockler and Halpern, is reviewed. For example, if someone wins an election 11-0, then each person who votes for him is less responsible for the victory than if he had won 6-5. Degree of blame takes into account an agent's epistemic state. Roughly speaking, the degree of blame of A for B is the expected degree of responsibility of A for B, taken over the epistemic state of an agent. Finally, the structural-equations definition of causality is compared to Wright's NESS test. Language: en

Investigation of the Granger Causal Relationship Between Macroeconomic Variables and Stock Prices in Kenya

The aim of this paper was to analyze the causal relationship between macroeconomic variables and stock prices in the VAR(Vector Autoregressive) modeling framework using secondary time series annual data from 1980 to 2012. Sim's causality test based on Granger definition of causality was used to test the causality relationship while OLS (Ordinary Least Squares) is used to test for any significant relationship. According to Granger causality test results, it is evident that movement in the macroeconomic variables had no significant effect on stock prices except for inflation rate; exchange rate and change in stock prices also seem to be an insignificant factor explaining part of the movement in the macroeconomic variables except for market interest rates. Also, the regression test result shows that all the macroeconomic variables are jointly significant in explaining the variations in stock prices. Hence, the findings imply that the causality between macroeconomic variables and stock prices runs unilaterally or entirely in one direction from inflation rate and exchange rate to stock prices and from stock prices to market interest rates. Thus, there is evidence to show that inflation rate and exchange rate are the cause of movement on stock prices and stock prices are the cause of movement of interest rates in Kenya.

A note on causality in reflexive Banach spaces.

AbstractIn this short note we discuss the causality of linear closable operators in reflexive Banach spaces. We show that in general causality is not preserved under closure procedures. We shall give an alternative definition of causality for closable operators by means of a certain continuity poperty that carries over to the closure. (© 2014 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Causal Effects in Mediation Modeling: An Introduction With Applications to Latent Variables

Causal inference in mediation analysis offers counterfactually based causal definitions of direct and indirect effects, drawing on research by Robins, Greenland, Pearl, VanderWeele, Vansteelandt, Imai, and others. This type of mediation effect estimation is little known and seldom used among analysts using structural equation modeling (SEM). The aim of this article is to describe the new analysis opportunities in a way that is accessible to SEM analysts and show examples of how to perform the analyses. An application is presented with an extension to a latent mediator measured with multiple indicators.

The Computational Complexity of Structure-Based Causality

Halpern and Pearl introduced a definition of actual causality; Eiter and Lukasiewicz showed that computing whether X = x is a cause of Y = y is NP-complete in binary models (where all variables can take on only two values) and \Sigma^P_2-complete in general models. In the final version of their paper, Halpern and Pearl slightly modified the definition of actual cause, in order to deal with problems pointed by Hopkins and Pearl. As we show, this modification has a nontrivial impact on the complexity of computing actual cause. To characterize the complexity, a new family D_k^P , k = 1,2,3,..., of complexity classes is introduced, which generalizes the class D^P introduced by Papadimitriou and Yannakakis (DP is just D^P_1). We show that the complexity of computing causality under the updated definition is D^P_2 -complete. Chockler and Halpern extended the definition of causality by introducing notions of responsibility and blame. The complexity of determining the degree of responsibility and blame using the original definition of causality was completely characterized. Again, we show that changing the definition of causality affects the complexity, and completely characterize it using the updated definition.

Definitions of causality in bond graph model for efficient simulation mechanism

Planar mechanism bond graph model is improved to adopt efficiency simulation needs, but it has complex causality, and each mechanism model needs to define causality. To make it regular, possibilities of causality in mechanism are analyzed. Element classes and joint class definitions in elements are made. Then definitions of causality for element modeling and for connections are established for efficient simulation mechanism bond graph model. These definitions make bond graph modeling regular and easier to be used in practice. The proposed method is suitable for cylinder-powered revolute joint mechanism.

"The end justifies the means." Historical realism and causality in modernity

Perceived all along, from the beginning of the modern era, causality in history cannot be abstracted from a reflection on "the reason and uses of States". Such realism engages with the heart of the arts of governance, the practices which form the basis for the adage "the end justifies the means". In order to assess the implications of this maxim on the definition of causality, this article examines the modalities of the description of historical facts, its usages and censoring (understood as a necessary means to aspired ends), and the calculation of the aleatory dimensions of politics.

Predicted causality in decision making: the role of culture.

DECISIONS AND HOW THEY ARE MADE In the wider sense, decision making is embedded in the problem-solving process and its many stages (Davidson and Sternberg, 2003; Guss et al., 2010). In the narrow sense, decisionmaking is understood as the ability to select one of several alternatives and to act accordingly (Guss, 2004). Previous research has often focused on decision making in relatively predictable environmentswith clear goals (e.g., expected utility theory of von Neumann and Morgenstern, 1944). In recent decades the focus has been on decision making heuristics, i.e., strategies or rules of thumb, applied in uncertain situations (e.g., Tversky and Kahneman, 1974; Simon, 1979; Gigerenzer and Gaissmaier, 2011). Causality plays an important role in many cognitive processes – and causal cognition is itself influenced by culture (e.g., Norenzayan and Nisbett, 2000; Medin and Atran, 2004; Beller et al., 2009; Bender and Beller, 2011; for a controversial discussion of causal cognition, see Sperber et al., 1995). Causality is especially important during the decision-making process, because the decision maker has to predict what consequences specific decisions bring about before making a decision. Causality refers here to the predicted decision options, that a specific planned action, when executed under specific circumstances, will have a specific predicted effect. This definition of causality refers to Aristotle’s causa efficiens, i.e., an action is the origin and will cause an intended effect. Our understanding of causality is a constructivist understanding, because causality refers to the causal predictions of the actor and sometimes the actor’s predicted probability of causal consequences might differ from a normative-mathematical probability of causal consequences. Predictions by actor and mathematical probability might be quite high (“As it is raining slightly, I will use the big umbrella and therefore not get wet during my walk”), but predictions by actor might be high and mathematical probability might be quite low (“when I buy a lottery ticket and use the birthdates of my family as lucky numbers, then I will win a million dollars”). Thus one could speak of predicted causality guiding the decision-making process. We are referring here to the predictions of the actor across domains. The selection of decision alternatives is dependent on several factors such as importance, urgency, and likelihood of success (e.g., Dorner, 2008; Dorner and Guss, 2013). First, the predictions regarding decision alternatives involve the estimation of how important an alternative is. The importance is related to the human needs and the decision alternative, for example, to drink a glass of water when extremely thirsty would be more important than the decision alternative to call a friend to chat. Thus, although decision making is a cognitive process, it is related to our human needs and motivational processes. Second, predictions regarding decision alternatives involve estimations of time and resulting urgency. If I am in my office and it is 5:30 pm, and I want to buy some groceries for the weekend and I know the store closes at 6:00 pm, and I know it takes me 15 min to get to the store, then the decision alternative “check and respond to emails” is perceived as less urgent (if the time estimation to check and respond to emails is longer than a few minutes which is usually the case). Third, predictions regarding decision alternatives involve estimations of how likely it is that the predicted consequences actually happen. I know 15 min is the time I need to go to the store and I know I need an hour to check my emails and to respond to them. This predicted likelihood of success is dependent on one’s competence: first the epistemic competence, i.e., the fact knowledge and experiential knowledge of the past; and second, the general competence, i.e., an estimation of one’s ability to act successfully in the given situation (Dorner, 2008). High general competence is reflected in high predicted likelihood of success for decision alternatives (“I can do this”). In other words, one believes in oneself and that translates into one’s ability to deal with situations successfully. Judging importance, urgency, and likelihood of success for decision alternatives can occur either automatically or deliberately, i.e., unconsciously or consciously. Automatically means that based on previous experiences in similar situations, the predictions and their results are known and attributed to the current situation. Often certain cues in the current situation trigger the memory of similar situations and connected with those the successful actions in those situations which can then be applied in the current situation (e.g., recognition-primed decision making according to Klein, 2008). If the current situation is a novel situation, then deliberations about possible

STATISTICAL CAUSALITY AND MARTINGALE REPRESENTATION PROPERTY WITH APPLICATION TO STOCHASTIC DIFFERENTIAL EQUATIONS

AbstractThe paper considers a statistical concept of causality in continuous time between filtered probability spaces, based on Granger’s definition of causality. This causality concept is connected with the preservation of the martingale representation property when the filtration is getting smaller. We also give conditions, in terms of causality, for every martingale to be a continuous semimartingale, and we consider the equivalence between the concept of causality and the preservation of the martingale representation property under change of measure. In addition, we apply these results to weak solutions of stochastic differential equations. The results can be applied to the economics of securities trading.

Cardiovascular control and time domain Granger causality: insights from selective autonomic blockade

We studied causal relations among heart period (HP), systolic arterial pressure (SAP) and respiration (R) according to the definition of Granger causality in the time domain. Autonomic pharmacological challenges were used to alter the complexity of cardiovascular control. Atropine (AT), propranolol and clonidine (CL) were administered to block muscarinic receptors, β-adrenergic receptors and centrally sympathetic outflow, respectively. We found that: (i) at baseline, HP and SAP interacted in a closed loop with a dominant causal direction from HP to SAP; (ii) pharmacological blockades did not alter the bidirectional closed-loop interactions between HP and SAP, but AT reduced the dominance of the causal direction from HP to SAP; (iii) at baseline, bidirectional interactions between HP and R were frequently found; (iv) the closed-loop relation between HP and R was unmodified by the administration of drugs; (v) at baseline, unidirectional interactions from R to SAP were often found; and (vi)while AT induced frequently an uncoupling between R and SAP, CL favoured bidirectional interactions. These results prove that time domain measures of Granger causality can contribute to the description of cardiovascular control by suggesting the temporal direction of the interactions and by separating different causality schemes (e.g. closed loop versus unidirectional relations).

A framework for assessing frequency domain causality in physiological time series with instantaneous effects

We present an approach for the quantification of directional relations in multiple time series exhibiting significant zero-lag interactions. To overcome the limitations of the traditional multivariate autoregressive (MVAR) modelling of multiple series, we introduce an extended MVAR (eMVAR) framework allowing either exclusive consideration of time-lagged effects according to the classic notion of Granger causality, or consideration of combined instantaneous and lagged effects according to an extended causality definition. The spectral representation of the eMVAR model is exploited to derive novel frequency domain causality measures that generalize to the case of instantaneous effects the known directed coherence (DC) and partial DC measures. The new measures are illustrated in theoretical examples showing that they reduce to the known measures in the absence of instantaneous causality, and describe peculiar aspects of directional interaction among multiple series when instantaneous causality is non-negligible. Then, the issue of estimating eMVAR models from time-series data is faced, proposing two approaches for model identification and discussing problems related to the underlying model assumptions. Finally, applications of the framework on cardiovascular variability series and multichannel EEG recordings are presented, showing how it allows one to highlight patterns of frequency domain causality consistent with well-interpretable physiological interaction mechanisms.

The Relation between Granger Causality and Directed Information Theory: A Review

This report reviews the conceptual and theoretical links between Granger causality and directed information theory. We begin with a short historical tour of Granger causality, concentrating on its closeness to information theory. The definitions of Granger causality based on prediction are recalled, and the importance of the observation set is discussed. We present the definitions based on conditional independence. The notion of instantaneous coupling is included in the definitions. The concept of Granger causality graphs is discussed. We present directed information theory from the perspective of studies of causal influences between stochastic processes. Causal conditioning appears to be the cornerstone for the relation between information theory and Granger causality. In the bivariate case, the fundamental measure is the directed information, which decomposes as the sum of the transfer entropies and a term quantifying instantaneous coupling. We show the decomposition of the mutual information into the sums of the transfer entropies and the instantaneous coupling measure, a relation known for the linear Gaussian case. We study the multivariate case, showing that the useful decomposition is blurred by instantaneous coupling. The links are further developed by studying how measures based on directed information theory naturally emerge from Granger causality inference frameworks as hypothesis testing.