Double Machine Learning Based Program Evaluation under Unconfoundedness

Michael Knaus

No 13051, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: This paper consolidates recent methodological developments based on Double Machine Learning (DML) with a focus on program evaluation under unconfoundedness. DML based methods leverage flexible prediction methods to control for confounding in the estimation of (i) standard average effects, (ii) different forms of heterogeneous effects, and (iii) optimal treatment assignment rules. We emphasize that these estimators build all on the same doubly robust score, which allows to utilize computational synergies. An evaluation of multiple programs of the Swiss Active Labor Market Policy shows how DML based methods enable a comprehensive policy analysis. However, we find evidence that estimates of individualized heterogeneous effects can become unstable.

Keywords: causal machine learning; individualized treatment rules; conditional average treatment effects; optimal policy learning; multiple treatments (search for similar items in EconPapers)
JEL-codes: C21 (search for similar items in EconPapers)
Pages: 57 pages
Date: 2020-03
New Economics Papers: this item is included in nep-big, nep-cmp and nep-exp
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Citations: View citations in EconPapers (21)

Published - published in: Econometrics Journal, 2022, 25 (3), 602-627

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Related works:
Working Paper: Double Machine Learning based Program Evaluation under Unconfoundedness (2022)
Working Paper: Double Machine Learning based Program Evaluation under Unconfoundedness (2020)
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