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

Font Size: 
Bias Reduction in a Matching Estimation of Treatment Effect
Maria Gabriella Campolo, Antonino Di Pino, Edoardo Otranto

Last modified: 2018-05-31


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


1. Austin, P. C.: An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioural Research, 46, 399-424 (2011)

2. Carneiro, P., Hansen, K. T., Heckman, J. J.: Estimating distributions of treatment effects with an application to the returns to schooling and measurement of the effects of uncertainty on college choice. International Economic Review 44(2), 361-422 (2003)

3. Harvey, A. C.: Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press (1990)

4. Heckman, J. J., Navarro-Lozano, S.: Using matching, instrumental variables, and control functions to estimate economic choice models. The Review of Economics and Statistics, 86(1), 30-57 (2004)

5. Rosenbaum, P. R, Rubin, D. B.: The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55 (1983)

6. Winship, C., Morgan S. L.: The estimation of causal effects from observational data. Annual Review of Sociology, 25, 659-706 (1999)

Full Text: PDF