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Causal Reasoning in Graphical Time Series Models
"... We propose a definition of causality for time series in terms of the effect of an intervention in one component of a multivariate time series on another component at some later point in time. Conditions for identifiability, comparable to the back–door and front–door criteria, are presented and can a ..."
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Cited by 12 (3 self)
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We propose a definition of causality for time series in terms of the effect of an intervention in one component of a multivariate time series on another component at some later point in time. Conditions for identifiability, comparable to the back–door and front–door criteria, are presented and can also be verified graphically. Computation of the causal effect is derived and illustrated for the linear case. 1
Graphical modelling of multivariate time series with latent variables
, 2005
"... Abstract. In time series analysis, inference about causee®ect relationships among multiple times series is commonly based on the concept of Granger causality, which exploits temporal structure to achieve causal ordering of dependent variables. One major problem in the application of Granger causali ..."
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Cited by 7 (2 self)
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Abstract. In time series analysis, inference about causee®ect relationships among multiple times series is commonly based on the concept of Granger causality, which exploits temporal structure to achieve causal ordering of dependent variables. One major problem in the application of Granger causality for the identi¯cation of causal relationships is the possible presence of latent variables that a®ect the measured components and thus lead to socalled spurious causalities. In this paper, we describe a new graphical approach for modelling the dependence structure of multivariate stationary time series that are a®ected by latent variables. Is is based on mixed graphs in which directed edges represent direct in°uences among the variables while dashed edgesdirected or undirectedindicate associations that are induced by latent variables. For Gaussian processes, this approach leads to vector autoregressive processes with errors that are not independent but correlated according to the dashed edges in the graph. We show that these models can be viewed as graphical ARMA models that satisfy the Granger causality restrictions encoded by general mixed graphs. We discuss identi¯ability of the parameters and illustrate the approach by an example. 1.
Fitting Graphical Interaction Models to Multivariate Time Series
"... Graphical interaction models have become an important tool for analysing multivariate time series. In these models, the interrelationships among the components of a time series are described by undirected graphs in which the vertices depict the components while the edges indictate possible dependenc ..."
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Cited by 6 (0 self)
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Graphical interaction models have become an important tool for analysing multivariate time series. In these models, the interrelationships among the components of a time series are described by undirected graphs in which the vertices depict the components while the edges indictate possible dependencies between the components. Current methods for the identification of the graphical structure are based on nonparametric spectral estimation, which prevents application of common model selection strategies. In this paper, we present a parametric approach for graphical interaction modelling of multivariate stationary time series. The proposed models generalize covariance selection models to the time series setting and are formulated in terms of inverse covariances. We show that these models correspond to vector autoregressive models under conditional independence constraints encoded by undirected graphs. Furthermore, we discuss maximum likelihood estimation based on Whittle’s approximation to the loglikelihood function and propose an iterative method for solving the resulting likelihood equations. The concepts are illustrated by an example.
Article Simulation Study of Direct Causality Measures in Multivariate Time Series
, 2013
"... entropy ..."
The Relation between Granger Causality and Directed Information Theory: A Review
 ENTROPY
, 2013
"... ..."
On Grangercausality and the effect of interventions in time series
, 2009
"... Abstract. We combine two approaches to causal reasoning. Granger–causality, on the one hand, is popular in fields like econometrics, where randomised experiments are not very common. Instead information about the dynamic development of a system is explicitly modelled and used to define potentially c ..."
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Cited by 2 (1 self)
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Abstract. We combine two approaches to causal reasoning. Granger–causality, on the one hand, is popular in fields like econometrics, where randomised experiments are not very common. Instead information about the dynamic development of a system is explicitly modelled and used to define potentially causal relations. On the other hand, the notion of causality as effect of interventions is predominant in fields like medical statistics or computer science. In this paper, we consider the effect of external, possibly multiple and sequential, interventions in a system of multivariate time series, the Granger–causal structure of which is taken to be known. We address the following questions: under what assumptions about the system and the interventions does Granger–causality inform us about the effectiveness of interventions, and when does the possibly smaller system of observable times series allow us to estimate this effect? For the latter we derive criteria that can be checked graphically and are similar to the back–door and front–door criteria of Pearl (1995). 1.
Identifying the Coupling Structure in Complex Systems through the Optimal Causation Entropy Principle
 ENTROPY
, 2014
"... ..."
Causal inference from time series: what can be learned from Granger causality? To appear in
 Westerståhl (eds), Proceedings of the 13th International Congress of Logic, Methodology and Philosophy of Science
, 2007
"... Abstract. In time series analysis, inference about causeeffect relationships among multiple time series is commonly based on the concept of Granger causality, which exploits temporal structure to achieve causal ordering of dependent variables. One major and well known problem in the application o ..."
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Cited by 1 (1 self)
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Abstract. In time series analysis, inference about causeeffect relationships among multiple time series is commonly based on the concept of Granger causality, which exploits temporal structure to achieve causal ordering of dependent variables. One major and well known problem in the application of Granger causality for the identification of causal relationships is the possible presence of latent variables that affect the measured components and thus lead to socalled spurious causalities. In this paper, we present a new graphical approach for describing and analysing Grangercausal relationships in multivariate time series that are possibly affected by latent variables. We show how such representations can be used for inductive causal learning from time series and discuss the underlying assumptions and their implications for causal learning. 1.
Graphical Gaussian Modelling of Multivariate Time Series with Latent Variables
"... In time series analysis, inference about causeeffect relationships among multiple times series is commonly based on the concept of Granger causality, which exploits temporal structure to achieve causal ordering of dependent variables. One major problem in the application of Granger causality for the ..."
Abstract
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In time series analysis, inference about causeeffect relationships among multiple times series is commonly based on the concept of Granger causality, which exploits temporal structure to achieve causal ordering of dependent variables. One major problem in the application of Granger causality for the identification of causal relationships is the possible presence of latent variables that affect the measured components and thus lead to socalled spurious causalities. In this paper, we describe a new graphical approach for modelling the dependence structure of multivariate stationary time series that are affected by latent variables. To this end, we introduce dynamic maximal ancestral graphs (dMAGs), in which each time series is represented by a single vertex. For Gaussian processes, this approach leads to vector autoregressive models with errors that are not independent but correlated according to the dashed edges in the graph. We discuss identifiability of the parameters and show that these models can be viewed as graphical ARMA models that satisfy the Granger causality restrictions encoded by the associated dynamic maximal ancestral graph. 1
RM/09/003 ON GRANGER–CAUSALITY AND THE EFFECT OF INTERVENTIONS IN TIME SERIES
, 2008
"... effect of interventions in time series ..."