Abstract：
With the rapid development of computer science, there is an increasing demand for the use of causal inference methods and machine learning in the research of endocrine disorders and their long-term health outcomes. However, studies on the effective and appropriate applications of these approaches in real-world data and clinical settings are still limited. This review will illustrate the use of causal inference and machine learning in epidemiological research within the field of endocrinology and metabolism. It will examine each concept of causal inference and machine learning through application examples of endocrine disorders. Subsequently, the paper will discuss the integration of machine learning within the causal inference framework, including (i) the estimation of treatment effects or the causal relationship between exposure and outcomes, and (ii) the evaluation of heterogeneity in such treatment effects (or exposure-outcome causal relationship) based on individuals\' characteristics. Accurately assessing causal relationships and their heterogeneity across different individuals is crucial not only for determining effective interventions, but also for the appropriate allocation of medical resources and reducing healthcare disparities. By illustrating some application examples in endocrinology, this review aims to enhance readers\' understanding and application of causal inference and machine learning in future epidemiological studies focusing on endocrine disorders.
摘要：
随着计算机科学的飞速发展,在内分泌失调及其长期健康结果的研究中，越来越需要使用因果推理方法和机器学习。然而,关于这些方法在实际数据和临床环境中的有效和适当应用的研究仍然有限.这篇综述将说明因果推理和机器学习在内分泌学和代谢领域的流行病学研究中的应用。它将通过内分泌失调的应用示例来检查因果推理和机器学习的每个概念。随后，本文将讨论机器学习在因果推理框架中的集成，包括(I)估计治疗效果或暴露与结果之间的因果关系，（ii）基于个体特征评估此类治疗效果（或暴露-结果因果关系）的异质性。准确评估不同个体之间的因果关系及其异质性不仅对于确定有效的干预措施至关重要，而且也是为了适当分配医疗资源和减少医疗保健差距。通过举例说明内分泌学中的一些应用实例，这篇综述旨在提高读者对因果推理和机器学习在未来以内分泌紊乱为重点的流行病学研究中的理解和应用。
