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: |
2020
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|---|---|
| Authors: | Goller, Daniel ; Lechner, Michael ; Moczall, Andreas ; Wolff, Joachim |
| Published in: |
Labour economics : official journal of the European Association of Labour Economists. - Amsterdam [u.a.] : Elsevier, ISSN 0927-5371, ZDB-ID 1167233-X. - Vol. 65.2020
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| Subject: | active labour market policy | causal machine learning | Programme evaluation | propensity score | radius matching | treatment effects | Deutschland | Germany | Arbeitsmarktpolitik | Labour market policy | Künstliche Intelligenz | Artificial intelligence | Kausalanalyse | Causality analysis | Wirkungsanalyse | Impact assessment | Matching | Schätztheorie | Estimation theory | Langzeitarbeitslosigkeit | Long-term unemployment | Mikroökonometrie | Microeconometrics | Schätzung | Estimation |
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1016/j.labeco.2020.101855 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10012522902