Gilthorpe Mark S, Tu Yu-Kang
1Leeds Institute for Data Analytics, University of Leeds, Leeds, LS2 9NL UK.
2Faculty of Medicine and Health, University of Leeds, Leeds, LS2 9LU UK.
Emerg Themes Epidemiol. 2020 Mar 11;17:1. doi: 10.1186/s12982-020-00089-7. eCollection 2020.
We commend Nickerson and Brown on their insightful exposition of the mathematical algebra behind Simpson's paradox, suppression and Lord's paradox; we also acknowledge there can be differences in how Lord's paradox is approached analytically, compared to Simpson's paradox and suppression, though not in every example of Lord's paradox. Furthermore, Simpson's paradox, suppression and Lord's paradox ask the same questions, seeking to understand if statistical adjustment is valid and meaningful, identifying which analytical option is correct. In our exposition of this, we focus on the perspective of context, which must invoke causal thinking. From a causal thinking perspective, Simpson's paradox, suppression and Lord's paradox present very similar analytical challenges.
我们赞扬尼克森和布朗对辛普森悖论、抑制效应和洛德悖论背后的数学代数进行了深刻的阐述;我们也承认,与辛普森悖论和抑制效应相比,在分析处理洛德悖论时可能存在差异,不过并非在洛德悖论的每个例子中都如此。此外,辛普森悖论、抑制效应和洛德悖论提出了相同的问题,即试图理解统计调整是否有效且有意义,确定哪种分析选项是正确的。在我们对此的阐述中,我们关注情境视角,这必然需要因果思维。从因果思维的角度来看,辛普森悖论、抑制效应和洛德悖论呈现出非常相似的分析挑战。