Average Adjusted Association: Efficient Estimation with High Dimensional Confounders
This provides a method for summarizing association measures in heterogeneous populations with high-dimensional confounders, applicable in various sampling scenarios, but it is incremental as it builds on existing log odds ratio and DML frameworks.
The paper tackles the problem of summarizing the log odds ratio as a function of confounders by proposing the Average Adjusted Association (AAA) and develops efficient double/debiased machine learning estimators for it, demonstrating practicality and effectiveness in real data and simulations.
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.