2017•EconometricsOpen access

Non-Causality Due to Included Variables

Umberto Triacca

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Abstract

The contribution of this paper is to investigate a particular form of lack of invariance of causality statements to changes in the conditioning information sets. Consider a discrete-time three-dimensional stochastic process z = ( x , y 1 , y 2 ) ′ . We want to study causality relationships between the variables in y = ( y 1 , y 2 ) ′ and x. Suppose that in a bivariate framework, we find that y 1 Granger causes x and y 2 Granger causes x, but these relationships vanish when the analysis is conducted in a trivariate framework. Thus, the causal links, established in a bivariate setting, seem to be spurious. Is this conclusion always correct? In this note, we show that the causal links, in the bivariate framework, might well not be ‘genuinely’ spurious: they could be reflecting causality from the vector y to x. Paradoxically, in this case, it is the non-causality in trivariate system that is misleading.

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What this paper is about

The contribution of this paper is to investigate a particular form of lack of invariance of causality statements to changes in the conditioning information sets. Consider a discrete-time three-dimensional stochastic process z = ( x , y 1 , y 2 ) ′ . We want to study causality relationships between the variables in y = ( y 1 , y 2 ) ′ and x. Suppose that in a bivariate framework, we find that y 1 Granger causes x and y 2 Granger causes x, but these relationships vanish when the analysis is conducted in a trivariate framework. Thus, the causal links, established in a bivariate setting, seem to be spurious. Is this conclusion always correct? In this note, we show that the causal links, in the bivariate framework, might well not be ‘genuinely’ spurious: they could be reflecting causality from the vector y to x. Paradoxically, in this case, it is the non-causality in trivariate system that is misleading.

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Available abstract

The contribution of this paper is to investigate a particular form of lack of invariance of causality statements to changes in the conditioning information sets. Consider a discrete-time three-dimensional stochastic process z = ( x , y 1 , y 2 ) ′ . We want to study causality relationships between the variables in y = ( y 1 , y 2 ) ′ and x. Suppose that in a bivariate framework, we find that y 1 Granger causes x and y 2 Granger causes x, but these relationships vanish when the analysis is conducted in a trivariate framework. Thus, the causal links, established in a bivariate setting, seem to be spurious. Is this conclusion always correct? In this note, we show that the causal links, in the bivariate framework, might well not be ‘genuinely’ spurious: they could be reflecting causality from the vector y to x. Paradoxically, in this case, it is the non-causality in trivariate system that is misleading.

Key concepts: Bivariate analysis, Spurious relationship, Causality (physics), Granger causality, Econometrics, Mathematics, Statistics, Physics

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