{"title":"The Cornfield Condition and Principle: A Critical Appraisal.","authors":"Tobias Freidling, Marco Piccininni","doi":"10.1353/obs.00005","DOIUrl":null,"url":null,"abstract":"<p><p>Cornfield, Haenszel, Hammond, Lilienfeld, Shimkin, and Wynder (1959)'s seminal work made two major contributions that shape causal inference and epidemiology to this day. First, Cornfield et al. provided the first example of quantitative sensitivity analysis for unmeasured confounding; a result that became subsequently known as the Cornfield condition. Second, they advocated for the use of relative effect measures for causal inference and absolute effect measures in public health. This recommendation is also referred to as the Cornfield principle. This commentary critically re-examines Cornfield et al.'s landmark contributions. With the benefit of hindsight, we formalize their arguments, identify shortcomings, and contextualize their claims in the light of recent research.</p>","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"12 2","pages":"179-192"},"PeriodicalIF":0.0000,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13600450/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Observational studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1353/obs.00005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Cornfield, Haenszel, Hammond, Lilienfeld, Shimkin, and Wynder (1959)'s seminal work made two major contributions that shape causal inference and epidemiology to this day. First, Cornfield et al. provided the first example of quantitative sensitivity analysis for unmeasured confounding; a result that became subsequently known as the Cornfield condition. Second, they advocated for the use of relative effect measures for causal inference and absolute effect measures in public health. This recommendation is also referred to as the Cornfield principle. This commentary critically re-examines Cornfield et al.'s landmark contributions. With the benefit of hindsight, we formalize their arguments, identify shortcomings, and contextualize their claims in the light of recent research.