Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/232645 
Year of Publication: 
2020
Series/Report no.: 
IZA Discussion Papers No. 13893
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
Estimation of the causal effect of a binary treatment on outcomes often requires conditioning on covariates to address selection on observed variables. This is not straightforward when one or more of the covariates are measured with error. Here, we present a new semi-parametric estimator that addresses this issue. In particular, we focus on inverse propensity score weighting estimators when the propensity score is of an unknown functional form and some covariates are subject to classical measurement error. Our proposed solution involves deconvolution kernel estimators of the propensity score and the regression function weighted by a deconvolution kernel density estimator. Simulations and replication of a study examining the impact of two financial literacy interventions on the business practices of entrepreneurs show our estimator to be valuable to empirical researchers.
Subjects: 
program evaluation
measurement error
propensity score
unconfoundedness
financial literacy
JEL: 
C18
C21
G21
G53
Document Type: 
Working Paper

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