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

• Published in: Emerging Themes in Epidemiology
• Main Author: Arah Onyebuchi A
• Format: Article in Journal/Newspaper
• Language: English
• Published: BMC 2008
• Subjects: Infectious and parasitic diseases; RC109-216; The Pointers
• Online Access: https://doi.org/10.1186/1742-7622-5-5; https://doaj.org/article/254bb9cd7a54453c8361a1f7a0489873

Abstract 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.