Propensity Score Weighting with Mismeasured Covariates: An Application to Two Financial Literacy Interventions

Hao Dong () and Daniel Millimet

JRFM, 2020, vol. 13, issue 11, 1-24

Abstract: Estimation of the causal effect of a binary treatment on outcomes often requires conditioning on covariates to address selection concerning 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.

Keywords: program evaluation; measurement error; propensity score; unconfoundedness; financial literacy (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Working Paper: Propensity Score Weighting with Mismeasured Covariates: An Application to Two Financial Literacy Interventions (2020)
Working Paper: Propensity Score Weighting with Mismeasured Covariates: An Application to Two Financial Literacy Interventions (2020)
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