论文标题
平均调整关联：高维混杂器的有效估计
Average Adjusted Association: Efficient Estimation with High Dimensional Confounders
论文作者
论文摘要
对数比值比是一个公认的度量，用于评估二元结果和暴露变量之间的关联。尽管使用了广泛使用，但关于如何通过平均来汇总对数的比值比作为混杂因素的函数的讨论有限。为了解决这个问题，我们提出了平均调整后的关联（AAA），这是针对观察到的混杂因素调整的异质人群中关联的摘要度量。为了促进它的使用，我们还开发了AAA的高效双重/辩护机器学习（DML）估计器。我们的DML估计器使用两种等效形式的有效影响函数，并且适用于各种采样场景，包括随机抽样，基于结果的采样和基于暴露的采样。通过实际的数据和模拟，我们证明了我们提出的估计器在测量AAA方面的实用性和有效性。
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.
