Causality in Econometric Modeling: From Theory to Structural Causal Modeling

Renzo Orsi, Michel Mouchart and Guillaume Wunsch
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Michel Mouchart: Université catholique de Louvain, LIDAM/ISBA, Belgium

No 2022024, LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: This paper examines different approaches for assessing causality as typically followed in econometrics and proposes a constructive perspective for improving statistical models elaborated in view of causal analysis. Without attempting to be exhaustive, this paper examines some of these approaches. Traditional structural modeling is first discussed. A distinction is then drawn between model-based and design-based approaches. Some more recent developments are examined next, namely history-friendly simulation and information-theory based approaches. Finally, in a constructive perspective, structural causal modeling (SCM) is presented, based on the concepts of mechanism and sub-mechanisms, and of recursive decomposition of the joint distribution of variables. This modeling strategy endeavors at representing the structure of the underlying data generating process. It operationalizes the concept of causation through the ordering and role-function of the variables in each of the intelligible sub-mechanisms.

Keywords: Structural modeling; exogeneity; causality; model-based and design-based approaches; recursive decomposition; history-friendly simulation; transfer entropy (search for similar items in EconPapers)
Pages: 30
Date: 2022-06-30
Note: In: Journal of Econometrics and Statistics, 2022, vol. 2(1), p. 61-90
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Related works:
Working Paper: Causality in econometric modeling. From theory to structural causal modeling (2020)
Working Paper: Causality in Econometric Modeling. From Theory to Structural Causal Modeling (2020)
Working Paper: Causality in econometric modeling. From theory to structural causal modeling (2020)
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