Year: 2023
Type: article
Abstract: Simulation studies are a powerful and important tool in epidemiologic teaching, especially for understanding causal inference. Simulations using the sufficient component cause framework can provide students key insights about causal mechanisms and sources of bias, but are not commonly used. To make them more accessible, we aim to provide an introduction and tutorial on developing and using these simulations, including an overview of translation from directed acyclic graphs and potential outcomes to sufficient component causal models, and a summary of the simulation approach. Using the applied question of the impact of educational attainment on dementia, we offer simple simulation examples and accompanying code to illustrate sufficient component cause-based simulations for four common causal structures (causation, confounding, selection bias, and effect modification) often introduced early in epidemiologic training. We show how sufficient component cause-based simulations illuminate both the causal processes and the mechanisms through which bias occurs, which can help enhance student understanding of these causal structures and the distinctions between them. We conclude with a discussion of considerations for using sufficient component cause-based simulations as a teaching tool.
Source: Frontiers in Epidemiology
Authors: Katrina L. Kezios, Eleanor Hayes-Larson
Institutions: Columbia University, University of California, Los Angeles
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,125
OpenAlex ID: W4388638700