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

Author

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  • Alex Coad
  • Dominik Janzing
  • Paul Nightingale

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.

Suggested Citation

  • Alex Coad & Dominik Janzing & Paul Nightingale, 2018. "Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables: Theory and applications," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, vol. 37(75), pages 779-808, March.
    • Handle: RePEc:col:000093:017128
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    References listed on IDEAS

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    Cited by:

    1. Cucculelli, Marco & Peruzzi, Valentina, 2020. "Innovation over the industry life-cycle. Does ownership matter?," Research Policy, Elsevier, vol. 49(1).
    2. Diego Norena-Chavez & Ruben Guevara, 2020. "Entrepreneurial Passion and Self-Efficacy as Factors Explaining Innovative Behavior: A Mediation Model," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(3), pages 352-373.

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    More about this item

    Keywords

    Causal inference; innovation surveys; machine learning; additive noisemodels; directed acyclic graphs;
    All these keywords.

    JEL classification:

    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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