Average Adjusted Association: Efficient Estimation with High Dimensional Confounders

Sung Jae Jun, Sokbae Lee
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:5980-5996, 2023.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v206-jun23a, title = {Average Adjusted Association: Efficient Estimation with High Dimensional Confounders}, author = {Jun, Sung Jae and Lee, Sokbae}, booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics}, pages = {5980--5996}, year = {2023}, editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem}, volume = {206}, series = {Proceedings of Machine Learning Research}, month = {25--27 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v206/jun23a/jun23a.pdf}, url = {https://proceedings.mlr.press/v206/jun23a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Average Adjusted Association: Efficient Estimation with High Dimensional Confounders %A Sung Jae Jun %A Sokbae Lee %B Proceedings of The 26th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2023 %E Francisco Ruiz %E Jennifer Dy %E Jan-Willem van de Meent %F pmlr-v206-jun23a %I PMLR %P 5980--5996 %U https://proceedings.mlr.press/v206/jun23a.html %V 206 %X 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.
APA
Jun, S.J. & Lee, S.. (2023). Average Adjusted Association: Efficient Estimation with High Dimensional Confounders. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 206:5980-5996 Available from https://proceedings.mlr.press/v206/jun23a.html.

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