{"title":"对未测量混杂的简单而敏锐的敏感性分析","authors":"J. Peña","doi":"10.1515/jci-2021-0041","DOIUrl":null,"url":null,"abstract":"Abstract We present a method for assessing the sensitivity of the true causal effect to unmeasured confounding. The method requires the analyst to set two intuitive parameters. Otherwise, the method is assumption free. The method returns an interval that contains the true causal effect and whose bounds are arbitrarily sharp, i.e., practically attainable. We show experimentally that our bounds can be tighter than those obtained by the method of Ding and VanderWeele, which, moreover, requires to set one more parameter than our method. Finally, we extend our method to bound the natural direct and indirect effects when there are measured mediators and unmeasured exposure–outcome confounding.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"42 1","pages":"1 - 17"},"PeriodicalIF":1.7000,"publicationDate":"2021-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Simple yet sharp sensitivity analysis for unmeasured confounding\",\"authors\":\"J. Peña\",\"doi\":\"10.1515/jci-2021-0041\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract We present a method for assessing the sensitivity of the true causal effect to unmeasured confounding. The method requires the analyst to set two intuitive parameters. Otherwise, the method is assumption free. The method returns an interval that contains the true causal effect and whose bounds are arbitrarily sharp, i.e., practically attainable. We show experimentally that our bounds can be tighter than those obtained by the method of Ding and VanderWeele, which, moreover, requires to set one more parameter than our method. Finally, we extend our method to bound the natural direct and indirect effects when there are measured mediators and unmeasured exposure–outcome confounding.\",\"PeriodicalId\":48576,\"journal\":{\"name\":\"Journal of Causal Inference\",\"volume\":\"42 1\",\"pages\":\"1 - 17\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2021-04-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Causal Inference\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1515/jci-2021-0041\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Causal Inference","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1515/jci-2021-0041","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Simple yet sharp sensitivity analysis for unmeasured confounding
Abstract We present a method for assessing the sensitivity of the true causal effect to unmeasured confounding. The method requires the analyst to set two intuitive parameters. Otherwise, the method is assumption free. The method returns an interval that contains the true causal effect and whose bounds are arbitrarily sharp, i.e., practically attainable. We show experimentally that our bounds can be tighter than those obtained by the method of Ding and VanderWeele, which, moreover, requires to set one more parameter than our method. Finally, we extend our method to bound the natural direct and indirect effects when there are measured mediators and unmeasured exposure–outcome confounding.
期刊介绍:
Journal of Causal Inference (JCI) publishes papers on theoretical and applied causal research across the range of academic disciplines that use quantitative tools to study causality.