ArticleInternational journal of epidemiology2022
Educational note: causal decomposition of population health differences using Monte Carlo integration and the g-formula.
Article in International journal of epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 33 citations in OpenAlex.
- Understanding higher substance use among early adolescent females: A decomposition analysis of adverse childhood experiences, bullying victimization, and depressive symptoms.Addictive behaviors reports · 2026Article
- Causal Inference in Health Disparities Research.Annual review of public health · 2026Review
- Demographic and biosocial determinants of beneficial trends in older adult cognition in the USA and England: the role of cohort succession.International journal of epidemiology · 2026Article
- Quantifying the contribution of social disconnection to the mortality gap associated with mental disorders: a decomposition analysis.European journal of epidemiology · 2026Article
- Three-Way Decomposition for Applied Health Equity Research: A Practical Tutorial to Understand and Address Inequalities.Prevention science : the official journal of the Society for Prevention Research · 2025Article
- Article
- A Review of the Causal Decomposition Framework for Modeling Interventions that Reduce Disparities.Current epidemiology reports · 2025Article
- Bayesian Mediation Analysis for Time-to-Event Outcome: Investigating Racial Disparity in Breast Cancer Survival.Communications in statistics: theory and methods · 2025Article
- Using a causal decomposition approach to estimate the contribution of employment to differences in mental health profiles between men and women.SSM - population health · 2024Article
- Path-specific causal decomposition analysis with multiple correlated mediator variables.Statistics in medicine · 2024Article
- Exploring sex variations in the incidence of cardiovascular events: a counterfactual decomposition analysis.European journal of public health · 2024Article
- Sherman Lecture: Are We Aiming at the Correct Targets to Reduce Disparities in Stroke Mortality? Celebration, Reflection, and Redirection.Journal of the American Heart Association · 2024Review
- Causal decomposition maps: An exploratory tool for designing area-level interventions aimed at reducing health disparities.Biometrical journal. Biometrische Zeitschrift · 2023Article
- Does Neighborhood Crime Mediate the Relationship Between Neighborhood Socioeconomic Status and Birth Outcomes? An Application of the Mediational G-Formula.American journal of epidemiology · 2023Article
- Effects of Medicare predictors in health disparities in the risk of Alzheimer's disease.Alzheimer's & dementia (New York, N. Y.)Article
- Causal Estimands and Multiply Robust Estimation of Mediated-Moderation.Multivariate behavioral researchArticle
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One key objective of the population health sciences is to understand why one social group has different levels of health and well-being compared with another. Whereas several methods have been developed in economics, sociology, demography, and epidemiology to answer these types of questions, a recent method introduced by Jackson and VanderWeele (2018) provided an update to decompositions by anchoring them within causal inference theory. In this paper, we demonstrate how to implement the causal decomposition using Monte Carlo integration and the parametric g-formula. Causal decomposition can help to identify the sources of differences across populations and provide researchers with a way to move beyond estimating inequalities to explaining them and determining what can be done to reduce health disparities. Our implementation approach can easily and flexibly be applied for different types of outcome and explanatory variables without having to derive decomposition equations. We describe the concepts of the approach and the practical steps and considerations needed to implement it. We then walk through a worked example in which we investigate the contribution of smoking to sex differences in mortality in South Korea. For this example, we provide both pseudocode and R code using our package, cfdecomp. Ultimately, we outline how to implement a very general decomposition algorithm that is grounded in counterfactual theory but still easy to apply to a wide range of situations.
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