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Reducing Bias in a Matching Estimation of Endogenous Treatment Effect

Author

Listed:
  • A. Di Pino
  • M.G. Campolo
  • E. Otranto

Abstract

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. A correction for the effects of this correlation on matching results leads to a reduction of bias. A Monte Carlo experiment and an empirical application using the LaLonde's experimental data set support this finding.

Suggested Citation

  • A. Di Pino & M.G. Campolo & E. Otranto, 2018. "Reducing Bias in a Matching Estimation of Endogenous Treatment Effect," Working Paper CRENoS 201805, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    • Handle: RePEc:cns:cnscwp:201805
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    References listed on IDEAS

    as
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    Keywords

    endogenous component of propensity scores; endogenous treatment; propensity score matching; State-Space Model;
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