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Does the estimation of the propensity score by machine learning improve matching estimation? : the case of Germany's programmes for long term unemployed
Goller, Daniel; Lechner, Michael; Moczall, Andreas; … - 2019
Matching-type estimators using the propensity score are the major workhorse in active labour market policy evaluation. This work investigates if machine learning algorithms for estimating the propensity score lead to more credible estimation of average treatment effects on the treated using a...
Persistent link: https://www.econbiz.de/10012060603
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Does the estimation of the propensity score by machine learning improve matching estimation? : the case of Germany's programmes for long term unemployed
Goller, Daniel; Lechner, Michael; Moczall, Andreas; … - 2019
Persistent link: https://www.econbiz.de/10012098947
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Does the estimation of the propensity score by machine learning improve matching estimation? : the case of Germany's programmes for long term unemployed
Goller, Daniel; Lechner, Michael; Moczall, Andreas; … - In: Labour economics : official journal of the European … 65 (2020)
Persistent link: https://www.econbiz.de/10012522902
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Does the estimation of the propensity score by machine learning improve matching estimation? : the case of Germany's programmes for long term unemployed
Goller, Daniel; Lechner, Michael; Moczall, Andreas; … - 2020
Matching-type estimators using the propensity score are the major workhorse in active labour market policy evaluation. This work investigates if machine learning algorithms for estimating the propensity score lead to more credible estimation of average treatment effects on the treated using a...
Persistent link: https://www.econbiz.de/10012165548