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.
Highlights
Empirical researchers in economics, finance, management, and other disciplines are often interested in the causal effect of a binary treatment on outcomes
We focus on the case when the propensity score is of an unknown functional form and some covariates are subject to classical measurement error
Each observation is characterized by the quadruple {Yj, Tj, Xj, Zj}, where Yj is the observed outcome, Tj is a binary indicator of the treatment received, Xj is a scalar covariate, and Zj is a d-dimensional vector of covariates
Summary
Propensity Score Weighting with Mismeasured Covariates: An Application to Two Financial Literacy Interventions. Any opinions expressed in this paper are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but IZA takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. Supported by the Deutsche Post Foundation, IZA runs the world’s largest network of economists, whose research aims to provide answers to the global labor market challenges of our time. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author
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