Understanding the role of hormones in pediatric growth: Insights from a double-debiased machine learning approach

Abstract

This study investigates the causal relationships between hormone levels and growth and development of children, focusing specifically on height disparities in cases of dwarfism. Besides utilizing double-debiased machine learning approach, the study integrates three alternative causal inference methods: partialing-out lasso linear regression, cross-fit partialing-out lasso linear regression, and post-double selection LASSO. These machine learning techniques are pivotal in identifying causal effects within observational data. The findings reveal a positive correlation between luteinizing hormone (LH) levels and adolescent height, while follicle-stimulating hormone (FSH) and the LH/FSH ratio show inverse correlations. The study underscores the significant role of hormone levels, particularly LH, in determining height, offering valuable insights that could guide future interventions or treatments for children and adolescents with dwarfism.

Keywords: Causal inference; Double-debiased Machine Learning; Height; Hormonal variations.