The log odds ratio is a well-established metric for evaluating the association between binary outcome and exposure variables. Despite its widespread use, there has been limited discussion on how to summarize the log odds ratio as a function of confounders through averaging. To address this issue, we propose the Average Adjusted Association (AAA), which is a summary measure of association in a heterogeneous population, adjusted for observed confounders. To facilitate the use of it, we also develop efficient double/debiased machine learning (DML) estimators of the AAA. Our DML estimators use two equivalent forms of the efficient influence function, and are applicable in various sampling scenarios, including random sampling, outcome-based sampling, and exposure-based sampling. Through real data and simulations, we demonstrate the practicality and effectiveness of our proposed estimators in measuring the AAA.

翻译：对数优势比是评估二元结局与暴露变量之间关联的经典指标。尽管应用广泛，但如何通过对混杂因素取平均来总结对数优势比函数的问题鲜有讨论。为解决这一问题，我们提出平均调整关联（AAA），该指标可在调整观察到的混杂因素后，对异质性人群中的关联进行概括性度量。为促进其应用，我们还开发了高效的AAA双重/去偏机器学习（DML）估计量。该估计量利用高效影响函数的两种等价形式，适用于随机抽样、基于结局的抽样和基于暴露的抽样等多种抽样场景。通过真实数据和模拟实验，我们证明了所提估计量在测量AAA方面的实用性和有效性。