Does the estimation of the propensity score by machine learning improve matching estimation? : the case of Germany's programmes for long term unemployed
Daniel Goller, Michael Lechner, Andreas Moczall, Joachim Wolff
| Year of publication: |
2019
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|---|---|
| Authors: | Goller, Daniel ; Lechner, Michael ; Moczall, Andreas ; Wolff, Joachim |
| Publisher: |
St. Gallen : School of Economics and Political Science, Department of Economics, University of St.Gallen |
| Subject: | Programme evaluation | active labour market policy | causal machine learning | treatment effects | radius matching | propensity score | Arbeitsmarktpolitik | Labour market policy | Deutschland | Germany | Kausalanalyse | Causality analysis | Wirkungsanalyse | Impact assessment | Künstliche Intelligenz | Artificial intelligence | Matching | Schätztheorie | Estimation theory | Langzeitarbeitslosigkeit | Long-term unemployment | Mikroökonometrie | Microeconometrics | Schätzung | Estimation |
freely available
| Extent: | 1 Online-Ressource (circa 43 Seiten) Illustrationen |
|---|---|
| Series: | Discussion paper / Universität Sankt Gallen, School of Economics and Political Science, Department of Economics. - Sankt Gallen : [Verlag nicht ermittelbar], ZDB-ID 2316967-9. - Vol. no. 2019, 10 (August 2019) |
| Type of publication: | Book / Working Paper |
| Type of publication (narrower categories): | Graue Literatur ; Non-commercial literature ; Arbeitspapier ; Working Paper |
| Language: | English |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10012098947