Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables: Theory and applications

Alex Coad, Dominik Janzing, Paul Nightingale

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

2 Citas (Scopus)


This paper presents a new statistical toolkit by applying three techniques for data-driven causal inference from the machine learning community that are little- known among economists and innovation scholars: a conditional independencebased approach, additive noise models, and non-algorithmic inference by hand. We include three applications to CIS data to investigate public funding schemes for R & D investment, information sources for innovation, and innovation expenditures and firm growth. Preliminary results provide causal interpretations of some previously- observed correlations. Our statistical 'toolkit' could be a useful complement to existing techniques.
Idioma originalEspañol
Páginas (desde-hasta)779-808
Número de páginas30
PublicaciónCuadernos de Economia (Colombia)
EstadoPublicada - 1 ene. 2018

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