Sufficient component cause simulations: an underutilized epidemiologic teaching tool
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-11-01
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| Series: | Frontiers in Epidemiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fepid.2023.1282809/full |
| _version_ | 1797629523740590080 |
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| author | Katrina L. Kezios Eleanor Hayes-Larson |
| author_facet | Katrina L. Kezios Eleanor Hayes-Larson |
| author_sort | Katrina L. Kezios |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T10:54:40Z |
| format | Article |
| id | doaj.art-e344a4d2256b45efbb85d1f92e98d2f7 |
| institution | Directory Open Access Journal |
| issn | 2674-1199 |
| language | English |
| last_indexed | 2024-03-11T10:54:40Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Epidemiology |
| spelling | doaj.art-e344a4d2256b45efbb85d1f92e98d2f72023-11-13T11:28:57ZengFrontiers Media S.A.Frontiers in Epidemiology2674-11992023-11-01310.3389/fepid.2023.12828091282809Sufficient component cause simulations: an underutilized epidemiologic teaching toolKatrina L. Kezios0Eleanor Hayes-Larson1Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, United StatesDepartment of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, United StatesSimulation 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.https://www.frontiersin.org/articles/10.3389/fepid.2023.1282809/fullsimulationcausal inferencebiassufficient component causeteachingconfounding |
| spellingShingle | Katrina L. Kezios Eleanor Hayes-Larson Sufficient component cause simulations: an underutilized epidemiologic teaching tool Frontiers in Epidemiology simulation causal inference bias sufficient component cause teaching confounding |
| title | Sufficient component cause simulations: an underutilized epidemiologic teaching tool |
| title_full | Sufficient component cause simulations: an underutilized epidemiologic teaching tool |
| title_fullStr | Sufficient component cause simulations: an underutilized epidemiologic teaching tool |
| title_full_unstemmed | Sufficient component cause simulations: an underutilized epidemiologic teaching tool |
| title_short | Sufficient component cause simulations: an underutilized epidemiologic teaching tool |
| title_sort | sufficient component cause simulations an underutilized epidemiologic teaching tool |
| topic | simulation causal inference bias sufficient component cause teaching confounding |
| url | https://www.frontiersin.org/articles/10.3389/fepid.2023.1282809/full |
| work_keys_str_mv | AT katrinalkezios sufficientcomponentcausesimulationsanunderutilizedepidemiologicteachingtool AT eleanorhayeslarson sufficientcomponentcausesimulationsanunderutilizedepidemiologicteachingtool |