Reducing Bias of the Matching Estimator of Treatment Effect in a Nonexperimental Evaluation Procedure

The traditional matching methods for the estimation of treatment parameters are often affected by selectivity bias due to the endogenous joint influence of latent factors on the assignment to treatment and on the outcome, especially in a cross-sectional framework. In this study, we show that the influence of unobserved factors involves a cross-correlation between the endogenous components of propensity scores and causal effects. We propose a correction for the bias effect of this correlation on matching results, adopting a state-space model to identify and estimate the unobserved factors. A Monte Carlo experiment supports this finding.