Bias Reducing Estimation of Treatment Effects in the Presence of Partially Distorted Data∗
نویسنده
چکیده
Estimating treatment effects via propensity scores is a widespread method in the treatment evaluation literature. However, covariates are often measured with error, leading to biased propensity score estimates. Using validation data to correct for this measurement error is a persistent topic in the limited dependent variable context yet assuming the former to be a random subsample with respect to the mismeasured population under inspection. This paper investigates the constellation that treatment and validation sample coincide. A new estimator for this setting is proposed. Its relative asymptotic dominance is shown by means of a theoretical condition. A Monte Carlo reveals that the new estimator performs better with respect to bias and mean squared error compared to the case of naive parametric modeling of the propensity score either using or ignoring the additional information.
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