Sufficient component cause simulations: an underutilized epidemiologic teaching tool

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.

Keywords: bias; causal inference; confounding; effect measure modification; selection bias; simulation; sufficient component cause; teaching.

Figure 1

DAG and SCC model correspondence.

Figure 2

Overview of SCC simulation approach.

Figure 3

DAG and SCC model for causation.

Figure 4

DAG and SCC model for confounding.

Figure 5

DAG and SCC model for collider bias.

Figure 6

DAG and SCC model for effect modification.