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 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.
| Translated title of the contribution | Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables: Theory and applications |
|---|---|
| Original language | Spanish |
| Pages (from-to) | 779-808 |
| Number of pages | 30 |
| Journal | Cuadernos de Economia (Colombia) |
| Volume | 37 |
| Issue number | 75 |
| DOIs | |
| Publication status | Published - 2018 |
| Externally published | Yes |
Keywords
- Additive noise models
- Apprentissage automatique (machine learning)
- Aprendizado automático (machine learning)
- Aprendizaje automático (machine learning)
- Causal inference
- Directed acyclic graphs
- Encuestas de innovación
- Enquêtes d'innovation
- Grafos acíclicos dirigidos
- Graphes acycliques dirigés
- Gráficos acíclicos dirigidos
- Inferencia causal
- Inferência causal
- Inférence causale
- Innovation surveys
- Machine learning
- Modelos de ruido aditivo
- Modelos de ruído aditivo
- Modèles de bruit additif
- Pesquisas sobre inovação
ASJC Scopus subject areas
- Arts and Humanities (miscellaneous)
- Social Sciences (miscellaneous)
- Economics, Econometrics and Finance(all)