{"title":"变量调整背后的认识论","authors":"Grayson L. Baird, Stephen L. Bieber","doi":"arxiv-2405.17224","DOIUrl":null,"url":null,"abstract":"It is often asserted that to control for the effects of confounders, one\nshould include the confounding variables of concern in a statistical model as a\ncovariate. Conversely, it is also asserted that control can only be concluded\nby design, where the results from an analysis can only be interpreted as\nevidence of an effect because the design controlled for the cause. To suggest\notherwise is said to be a fallacy of cum hoc ergo propter hoc. Obviously, these\ntwo assertions create a conundrum: How can the effect of confounder be\ncontrolled for with analysis instead of by design without committing cum hoc\nergo propter hoc? The present manuscript answers this conundrum.","PeriodicalId":501323,"journal":{"name":"arXiv - STAT - Other Statistics","volume":"97 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Epistemology behind Covariate Adjustment\",\"authors\":\"Grayson L. Baird, Stephen L. Bieber\",\"doi\":\"arxiv-2405.17224\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"It is often asserted that to control for the effects of confounders, one\\nshould include the confounding variables of concern in a statistical model as a\\ncovariate. Conversely, it is also asserted that control can only be concluded\\nby design, where the results from an analysis can only be interpreted as\\nevidence of an effect because the design controlled for the cause. To suggest\\notherwise is said to be a fallacy of cum hoc ergo propter hoc. Obviously, these\\ntwo assertions create a conundrum: How can the effect of confounder be\\ncontrolled for with analysis instead of by design without committing cum hoc\\nergo propter hoc? The present manuscript answers this conundrum.\",\"PeriodicalId\":501323,\"journal\":{\"name\":\"arXiv - STAT - Other Statistics\",\"volume\":\"97 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - STAT - Other Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2405.17224\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - STAT - Other Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2405.17224","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
人们通常认为,要控制混杂因素的影响,就应在统计模型中将相关的混杂变量作为一个变量。反之,也有人断言,只有通过设计才能得出控制的结论,即由于设计控制了原因,分析结果只能被解释为效果的证据。反之,则是 "既成事实"(cum hoc ergo propter hoc)的谬误。很明显,这两个论断造成了一个难题:如何通过分析而不是设计来控制混杂因素的影响,而又不犯兼有因果关系的谬误?本手稿回答了这一难题。
It is often asserted that to control for the effects of confounders, one
should include the confounding variables of concern in a statistical model as a
covariate. Conversely, it is also asserted that control can only be concluded
by design, where the results from an analysis can only be interpreted as
evidence of an effect because the design controlled for the cause. To suggest
otherwise is said to be a fallacy of cum hoc ergo propter hoc. Obviously, these
two assertions create a conundrum: How can the effect of confounder be
controlled for with analysis instead of by design without committing cum hoc
ergo propter hoc? The present manuscript answers this conundrum.