Bias Reduction in a Matching Estimation of Treatment Effect

The traditional matching methods for the estimation of the 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 the propensity scores and causal effects. A correction for the effects of this correlation on matching results leads to a reduction of bias. A Monte Carlo experiment supports this finding.