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

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.

Enhancing skeletal age estimation accuracy using support vector regression models.

Objective: The objective of the study was to determine if support vector regression (SVR) models could enhance the accuracy of skeletal age estimation compared to original metrics.

Method: The study used a dataset of 5,018 individuals from Wuhan, spanning ages 1 to 17. Optimal model parameters were found using cross-validation and grid search techniques.

Will the substitution of capital for labor increase the use of chemical fertilizer in agriculture? Analysis based on provincial panel data in China.

Major changes have taken place in the agricultural factor input structure in China, which will inevitably affect fertilizer input in agriculture. However, there is no consistent conclusion about the impact of capital and labor input on chemical fertilizer input. This paper employed directed acyclic graphs (DAGs) to clarify the influence of capital-labor input structure on the use of chemical fertilizer.

Prediction and Severity Ratings of COVID-19 in the United States.

Objectives: The objectives of this study is to predict the possible trajectory of coronavirus disease 2019 (COVID-19) spread in the United States. Prediction and severity ratings of COVID-19 are essential for pandemic control and economic reopening in the United States.

Method: In this study, we apply the logistic and Gompertz model to evaluate possible turning points of the COVID-19 pandemic in different regions.