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We explore how the use of the Gradient Boosting Machine (GBM) method to compute propensity scores may improve the estimation of the Quantile Treatment Effect (QTE) in the case of a binary treatment and a high dimensional dataset. To validate the procedure we provide a simulation study on several scenarios and apply the method to estimate the wage gap between workers in the informal and formal sectors in South Africa at different quantiles of the wage distribution.

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Quantile Treatment Effect and GBM: An Approach Based on Propensity Score

  • 会议论文

  • 发表日期：

    2025年

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  • Francesco Dotto,
  • Francesco Giuli,
  • Margherita Scarlato,
  • Francesco Bloise

摘要

We explore how the use of the Gradient Boosting Machine (GBM) method to compute propensity scores may improve the estimation of the Quantile Treatment Effect (QTE) in the case of a binary treatment and a high dimensional dataset. To validate the procedure we provide a simulation study on several scenarios and apply the method to estimate the wage gap between workers in the informal and formal sectors in South Africa at different quantiles of the wage distribution.