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

Alex Coad (), Dominik Janzing and Paul Nightingale

Revista Cuadernos de Economia, 2018, vol. 37, issue 75, 779-808

Abstract: 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 independence based 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.

Keywords: Causal inference; innovation surveys; machine learning; additive noisemodels; directed acyclic graphs (search for similar items in EconPapers)
JEL-codes: C21 O30 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:col:000093:017128

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