Sequential Matching Estimation of Dynamic Causal Models

Michael Lechner

No 1042, IZA Discussion Papers from IZA Network @ LISER

Abstract: This paper proposes sequential matching and inverse selection probability weighting to estimate dynamic causal effects. The sequential matching estimators extend simple, matching estimators based on propensity scores for static causal analysis that have been frequently applied in the evaluation literature. A Monte Carlo study shows that the suggested estimators perform well in small and medium size samples. Based on the application of the sequential matching estimators to an empirical problem - an evaluation study of the Swiss active labour market policies - some implementational issues are discussed and results are provided.

Keywords: nonparametric identification; dynamic treatment effects; causal effects; sequential randomisation; programme evaluation; panel data (search for similar items in EconPapers)
JEL-codes: C40 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2004-03
New Economics Papers: this item is included in nep-dev and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (54)

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Related works:
Working Paper: Sequential Matching Estimation of Dynamic Causal Models (2004)
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Persistent link: https://EconPapers.repec.org/RePEc:iza:izadps:dp1042

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