Publicado

2018-12-01

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

Herramientas para la inferencia causal de encuestas de innovación de corte transversal con variables continuas o discretas: Teoría y aplicaciones

Ferramentas para a inferência causal de pesquisas de inovação de corte transversal com variáveis contínuas ou discretas: teoria e aplicações

DOI:

https://doi.org/10.15446/cuad.econ.v37n75.69832

Palabras clave:

Causal inference, innovation surveys, machine learning, additive noise models, directed acyclic graphs (en)
inferencia causal, encuestas de innovación, aprendizaje automático (machine learning), modelos de ruido aditivo, grafos acíclicos dirigidos (es)
inferência causal, pesquisas sobre inovação, aprendizado automático (machine learning), modelos de ruído aditivo, gráficos acíclicos dirigidos (pt)

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Autores/as

  • Alex Coad Pontificia Universidad Católica del Perú
  • Dominik Janzing Causal Consulting
  • Paul Nightingale University of Sussex

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.

Este artículo presenta un nuevo conjunto de herramientas estadísticas al aplicar tres técnicas de inferencia causal basada en datos tomadas de la comunidad del aprendizaje automático (maching learning) y que son poco conocidas entre los economistas y los académicos de la innovación: un enfoque condicional basado en la independencia, modelos de ruido aditivo e inferencia no algorítmica a mano. Incluimos tres aplicaciones a los datos de la CIS —la encuesta de la comunidad sobre la innovación— para investigar los modelos de financiación pública para inversión en investigación y desarrollo, fuentes de información para la innovación, y gastos de innovación y crecimiento empresarial. Los resultados preliminares proporcionan interpretaciones causales de algunas correlaciones observadas previamente. Nuestro conjunto de herramientas estadísticas podría ser un complemento útil a las técnicas existentes.
Este artigo apresenta um novo conjunto de ferramentas estatísticas aplicando três técnicas de inferência causal baseadas em dados extraídos da comunidade de aprendizado automático (maching learning) e que são pouco conhecidas entre economistas e estudiosos da inovação: uma abordagem condicional baseada na independência, modelos aditivos de ruído e inferência não algorítmica à mão. Incluímos três aplicativos para os dados da CIS — a pesquisa da comunidade sobre inovação — para investigar os modelos de financiamento público para investimento em pesquisa e desenvolvimento, fontes de informação para inovação e gastos com inovação e crescimento de negócios. Os resultados preliminares fornecem interpretações causais de algumas correlações observadas anteriormente. Nosso conjunto de ferramentas estatísticas pode ser um complemento útil para as técnicas existentes.

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Cómo citar

APA

Coad, A., Janzing, D. y Nightingale, P. (2018). Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables: Theory and applications. Cuadernos de Economía, 37(75), 779–808. https://doi.org/10.15446/cuad.econ.v37n75.69832

ACM

[1]
Coad, A., Janzing, D. y Nightingale, P. 2018. Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables: Theory and applications. Cuadernos de Economía. 37, 75 (dic. 2018), 779–808. DOI:https://doi.org/10.15446/cuad.econ.v37n75.69832.

ACS

(1)
Coad, A.; Janzing, D.; Nightingale, P. Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables: Theory and applications. Cuadernos 2018, 37, 779-808.

ABNT

COAD, A.; JANZING, D.; NIGHTINGALE, P. Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables: Theory and applications. Cuadernos de Economía, [S. l.], v. 37, n. 75, p. 779–808, 2018. DOI: 10.15446/cuad.econ.v37n75.69832. Disponível em: https://revistas.unal.edu.co/index.php/ceconomia/article/view/69832. Acesso em: 23 abr. 2024.

Chicago

Coad, Alex, Dominik Janzing, y Paul Nightingale. 2018. «Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables: Theory and applications». Cuadernos De Economía 37 (75):779-808. https://doi.org/10.15446/cuad.econ.v37n75.69832.

Harvard

Coad, A., Janzing, D. y Nightingale, P. (2018) «Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables: Theory and applications», Cuadernos de Economía, 37(75), pp. 779–808. doi: 10.15446/cuad.econ.v37n75.69832.

IEEE

[1]
A. Coad, D. Janzing, y P. Nightingale, «Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables: Theory and applications», Cuadernos, vol. 37, n.º 75, pp. 779–808, dic. 2018.

MLA

Coad, A., D. Janzing, y P. Nightingale. «Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables: Theory and applications». Cuadernos de Economía, vol. 37, n.º 75, diciembre de 2018, pp. 779-08, doi:10.15446/cuad.econ.v37n75.69832.

Turabian

Coad, Alex, Dominik Janzing, y Paul Nightingale. «Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables: Theory and applications». Cuadernos de Economía 37, no. 75 (diciembre 1, 2018): 779–808. Accedido abril 23, 2024. https://revistas.unal.edu.co/index.php/ceconomia/article/view/69832.

Vancouver

1.
Coad A, Janzing D, Nightingale P. Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables: Theory and applications. Cuadernos [Internet]. 1 de diciembre de 2018 [citado 23 de abril de 2024];37(75):779-808. Disponible en: https://revistas.unal.edu.co/index.php/ceconomia/article/view/69832

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CrossRef citations2

1. Jacob Rubæk Holm, Edward Lorenz. (2022). The impact of artificial intelligence on skills at work in Denmark. New Technology, Work and Employment, 37(1), p.79. https://doi.org/10.1111/ntwe.12215.

2. Marco Cucculelli, Valentina Peruzzi. (2020). Innovation over the industry life-cycle. Does ownership matter?. Research Policy, 49(1), p.103878. https://doi.org/10.1016/j.respol.2019.103878.

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