  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.
