The role of causal reasoning in understanding Simpson's paradox, Lord's paradox, and the suppression effect: Covariate selection in the analysis of observational studies
Tu et al present an analysis of the equivalence of three paradoxes, namely, Simpson's, Lord's, and the suppression phenomena. They conclude that all three simply reiterate the occurrence of a change in the association of any two variables when a third variable is statistically controlled f...
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ftcdlib:qt23t2283r 2023-05-15T18:32:42+02:00 The role of causal reasoning in understanding Simpson's paradox, Lord's paradox, and the suppression effect: Covariate selection in the analysis of observational studies Arah, OA 2008-03-20 application/pdf http://www.escholarship.org/uc/item/23t2283r english eng eScholarship, University of California qt23t2283r http://www.escholarship.org/uc/item/23t2283r public Arah, OA. (2008). The role of causal reasoning in understanding Simpson's paradox, Lord's paradox, and the suppression effect: Covariate selection in the analysis of observational studies. Emerging Themes in Epidemiology, 5. doi:10.1186/1742-7622-5-5. UCLA: Retrieved from: http://www.escholarship.org/uc/item/23t2283r article 2008 ftcdlib https://doi.org/10.1186/1742-7622-5-5 2018-07-13T22:52:26Z Tu et al present an analysis of the equivalence of three paradoxes, namely, Simpson's, Lord's, and the suppression phenomena. They conclude that all three simply reiterate the occurrence of a change in the association of any two variables when a third variable is statistically controlled for. This is not surprising because reversal or change in magnitude is common in conditional analysis. At the heart of the phenomenon of change in magnitude, with or without reversal of effect estimate, is the question of which to use: the unadjusted (combined table) or adjusted (sub-table) estimate. Hence, Simpson's paradox and related phenomena are a problem of covariate selection and adjustment (when to adjust or not) in the causal analysis of non-experimental data. It cannot be overemphasized that although these paradoxes reveal the perils of using statistical criteria to guide causal analysis, they hold neither the explanations of the phenomenon they depict nor the pointers on how to avoid them. The explanations and solutions lie in causal reasoning which relies on background knowledge, not statistical criteria. © 2008 Arah; licensee BioMed Central Ltd. Article in Journal/Newspaper The Pointers University of California: eScholarship Emerging Themes in Epidemiology 5 1 |
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Tu et al present an analysis of the equivalence of three paradoxes, namely, Simpson's, Lord's, and the suppression phenomena. They conclude that all three simply reiterate the occurrence of a change in the association of any two variables when a third variable is statistically controlled for. This is not surprising because reversal or change in magnitude is common in conditional analysis. At the heart of the phenomenon of change in magnitude, with or without reversal of effect estimate, is the question of which to use: the unadjusted (combined table) or adjusted (sub-table) estimate. Hence, Simpson's paradox and related phenomena are a problem of covariate selection and adjustment (when to adjust or not) in the causal analysis of non-experimental data. It cannot be overemphasized that although these paradoxes reveal the perils of using statistical criteria to guide causal analysis, they hold neither the explanations of the phenomenon they depict nor the pointers on how to avoid them. The explanations and solutions lie in causal reasoning which relies on background knowledge, not statistical criteria. © 2008 Arah; licensee BioMed Central Ltd. |
format |
Article in Journal/Newspaper |
author |
Arah, OA |
spellingShingle |
Arah, OA The role of causal reasoning in understanding Simpson's paradox, Lord's paradox, and the suppression effect: Covariate selection in the analysis of observational studies |
author_facet |
Arah, OA |
author_sort |
Arah, OA |
title |
The role of causal reasoning in understanding Simpson's paradox, Lord's paradox, and the suppression effect: Covariate selection in the analysis of observational studies |
title_short |
The role of causal reasoning in understanding Simpson's paradox, Lord's paradox, and the suppression effect: Covariate selection in the analysis of observational studies |
title_full |
The role of causal reasoning in understanding Simpson's paradox, Lord's paradox, and the suppression effect: Covariate selection in the analysis of observational studies |
title_fullStr |
The role of causal reasoning in understanding Simpson's paradox, Lord's paradox, and the suppression effect: Covariate selection in the analysis of observational studies |
title_full_unstemmed |
The role of causal reasoning in understanding Simpson's paradox, Lord's paradox, and the suppression effect: Covariate selection in the analysis of observational studies |
title_sort |
role of causal reasoning in understanding simpson's paradox, lord's paradox, and the suppression effect: covariate selection in the analysis of observational studies |
publisher |
eScholarship, University of California |
publishDate |
2008 |
url |
http://www.escholarship.org/uc/item/23t2283r |
genre |
The Pointers |
genre_facet |
The Pointers |
op_source |
Arah, OA. (2008). The role of causal reasoning in understanding Simpson's paradox, Lord's paradox, and the suppression effect: Covariate selection in the analysis of observational studies. Emerging Themes in Epidemiology, 5. doi:10.1186/1742-7622-5-5. UCLA: Retrieved from: http://www.escholarship.org/uc/item/23t2283r |
op_relation |
qt23t2283r http://www.escholarship.org/uc/item/23t2283r |
op_rights |
public |
op_doi |
https://doi.org/10.1186/1742-7622-5-5 |
container_title |
Emerging Themes in Epidemiology |
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5 |
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1 |
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1766216892486254592 |