Confounding and control in a multivariate system. An issue in causal attribution

Federica Russo (), Michel Mouchart () and Guillaume Wunsch ()
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Federica Russo: Dipartimento di Studi Umanistici, Università degli Studi di Ferrara, Italy
Michel Mouchart: Université catholique de Louvain, ISBA and CORE, Belgium
Guillaume Wunsch: Université catholique de Louvain, Demography, Belgium; Royal Academy of Sciences, Belgium

No 2013068, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: It is widely agreed that, in establishing whether variable X causes variable Y, a third variable Z may confound the relation and thus hinder causal assessment. The solution developed within the ‘traditional’ framework is to control for any third variable, susceptible of confounding the relation between X and Y. This paper examines complex systems of variables, characterised by multiple causes and multiple effects. The paper advances the view that in such contexts confounding is a moot issue, under a suitable specification of the causal model. When networks of causal relations are considered, possible confounders are included in the appropriate causal paths from the causes to the outcome. The challenge for the model builder then amounts to developing a structural model that specifies the role of variables in each path, rather than just controlling for possible confounders.

Keywords: causality; confounding; control; structural modelling (search for similar items in EconPapers)
Date: 2013-12-31
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Citations: View citations in EconPapers (2)

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