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• W2912650072 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." @default.
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• W2912650072 date "2018-12-01" @default.
• W2912650072 modified "2023-09-23" @default.
• W2912650072 title "Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables: Theory and applications" @default.
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• W2912650072 doi "https://doi.org/10.15446/cuad.econ.v37n75.69832" @default.
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