Average Adjusted Association: Efficient Estimation with High Dimensional Confounders ...

• Main Authors: Jun, Sung Jae, Lee, Sokbae
• Format: Report
• Language: unknown
• Published: arXiv 2022
• Subjects: Methodology stat.ME; Machine Learning cs.LG; Econometrics econ.EM; Machine Learning stat.ML; FOS Computer and information sciences; FOS Economics and business; DML
• Online Access: https://dx.doi.org/10.48550/arxiv.2205.14048; https://arxiv.org/abs/2205.14048

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. ... : 35 pages, 3 tables ...