Resumen

COAD, Alex; JANZING, Dominik  y  NIGHTINGALE, Paul. TOOLS FOR CAUSAL INFERENCE FROM CROSS-SECTIONAL INNOVATION SURVEYS WITH CONTINUOUS OR DISCRETE VARIABLES: THEORY AND APPLICATIONS. Cuad. Econ. [online]. 2018, vol.37, n.spe75, pp.779-807. ISSN 0121-4772.  https://doi.org/10.15446/cuad.econ.v37n75.69832.

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.

JEL: O30, C21.

Palabras clave : Causal inference; innovation surveys; machine learning; additive noise models; directed acyclic graphs..

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