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.

Dotto, F., Giuli, F., Scarlato, M., Bloise, F. (2025). Quantile Treatment Effect and GBM: an approach based on propensity score. In Methodological and Applied Statistics and Demography – SIS 2024 Short Papers.

Quantile Treatment Effect and GBM: an approach based on propensity score

Francesco Dotto
;
Francesco Giuli;Margherita Scarlato;
2025-01-01

Abstract

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.
2025
Dotto, F., Giuli, F., Scarlato, M., Bloise, F. (2025). Quantile Treatment Effect and GBM: an approach based on propensity score. In Methodological and Applied Statistics and Demography – SIS 2024 Short Papers.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/498336
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