Bias Reduction in a Matching Estimation of Treatment Effect

Maria Gabriella Campolo; Antonino Di Pino; Edoardo Otranto

Open Conference Systems, 50th Scientific meeting of the Italian Statistical Society

Maria Gabriella Campolo, Antonino Di Pino, Edoardo Otranto

Last modified: 2018-05-31

Abstract

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

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