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Robust estimation of a marginal causal effect on the binary outcome using propensity score matching
Journal of the Korean Data & Information Science Society 2024;35:161-77
Published online January 31, 2024;  https://doi.org/10.7465/jkdi.2024.35.1.161
© 2024 Korean Data and Information Science Society.

Jaeho Jeong1 · Young Min Kim2

12Department of Statistics, Kyungpook National University
Correspondence to: 1 Doctor program, Department of Statistics, Kyungpook National University, Daegu 41566, Korea.
2 Corresponding author: Associate professor, Department of Statistics, Kyungpook National University, Daegu 41566, Korea. E-mail: kymmyself@knu.ac.kr
Received November 14, 2023; Revised November 28, 2023; Accepted December 6, 2023.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Observational studies encounter self-selection bias due to non-randomized treatment assignments. To solve the problem of the self-selection bias, we employ the propensity score matching approach to compare non-randomized treatment and control groups and estimate the robust marginal treatment effect. In this study, we consider the double-adjustment and g-computation methods to reduce residual confounding bias, target bias, and propensity score model bias, and the caliper method to address the confounding bias. We compute the robust marginal causal effect of smoking on depressive symptoms to investigate the relationship between smoking and depressive symptoms using the 8th Korean National Health and Nutrition Examination Survey data.
Keywords : Double-adjustment, g-computation, Korea National Health and Nutrition Examination Survey, propensity score matching.