Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/245885 
Year of Publication: 
2020
Series/Report no.: 
Quaderni - Working Paper DSE No. 1143
Publisher: 
Alma Mater Studiorum - Università di Bologna, Dipartimento di Scienze Economiche (DSE), Bologna
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.
Subjects: 
structural modeling
exogeneity
causality
model-based anddesign-based approaches
recursive decomposition
history-friendly simulation
transfer entropy
JEL: 
C01
C03
C15
C18
C51
C54
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Working Paper

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