Causal effects of smoking on depressive symptoms: the 8th Korea national health and nutrition examination survey †
Jaeho Jeong1 · Young Min Kim2
12Department of Statistics, Kyungpook National University
Correspondence to: † This work was supported by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning(KETEP) granted financial resources from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20204010600060)
1 Master program, Department of Statistics, Kyungpook National University, Daegu, 41566, Korea
2 Associate professor, Department of Statistics, Kyungpook National University, Daegu, 41566, Korea. E-mail: kymmyself@knu.ac.kr
1 Master program, Department of Statistics, Kyungpook National University, Daegu, 41566, Korea
2 Associate professor, Department of Statistics, Kyungpook National University, Daegu, 41566, Korea. E-mail: kymmyself@knu.ac.kr
Received October 4, 2022; Revised October 30, 2022; Accepted October 30, 2022.
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
- Several studies have been conducted on the relationship between smoking and depressive symptoms using observational study data. This manuscript examines if smoking has a causal effect on depressive symptoms using the 8th Korea national health and nutrition examination survey data. We conduct propensity score matching to reduce the self-selection bias of the observational study through balancing covariates to estimate the average treatment effect for the treatment group. In the propensity score matching process, this paper focuses on two major cautions. First, if the outcome variable is binary, the marginal effect may not coincide with the conditional effect. Thus, we use G-computation to estimate the marginal effect, robustly. Second, in the complex survey sampling data, we consider complex survey design when the target population is entire, not the group of respondents. The result shows that smoking can lead to an increase in depressive symptoms based on the causal inference approach, and it is the same between the entire population and the group of survey respondents.
- Keywords : Marginal causal effect, Korea national health and nutrition examination survey, propensity score matching, complex survey design.
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