Template-Type: ReDIF-Paper 1.0
Author-Name: De Mier Manuel
Author-Name-First: Manuel
Author-Name-Last: De Mier
Author-Name: Delbianco Fernando
Author-Name-First: Fernando
Author-Name-Last: Delbianco
Author-Name: Tohmé Fernando
Author-Name-First: Fernando
Author-Name-Last: Tohmé
Author-Name: Patrizio Luisina
Author-Name-First: Luisina
Author-Name-Last: Patrizio
Author-Name: Rodriguez Facundo
Author-Name-First: Facundo
Author-Name-Last: Luisina
Author-Name: Stéfani Mauro
Author-Name-First: Mauro
Author-Name-Last: Stéfani
Title: Causality by Vote: Aggregating Evidence on Causal Relations in Economic Growth Processes
Abstract: In this paper we investigate the performance of five causality-detection methods and how their results can be aggregated when multiple units are considered in a panel data setting. The aggregation procedure employs voting rules for determining which causal paths are identified for the sample population. Using simulated and real-world panel data, we show the performance of these methods in detecting the correct causal paths in comparison to a benchmark that comprises a standard representation of growth processes as ground truth model. We find that the results may be better when only simulated, instead of real-world, data are analyzed. While this may suggest that the methods presented here are currently incapable of detecting causal links, it is plausible that the ground ``truth'' may incorporate false relations.
Length:  19 pages
Creation-Date:  2023-11
File-URL: https://aaep.org.ar/works/works2023/4641.pdf
File-Format: Application/pdf
Number: 4641
Classification-JEL: C18, C43
Handle: RePEc:aep:anales:4641