[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-665387-105":59,"doc-detail-665387-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","causal-diagrams-for-epidemiologic-research","Causal Diagrams for Epidemiologic Research","","Causal diagrams support epidemiologic causal inference by identifying which variables must be measured and controlled to obtain unconfounded effect estimates. The text explains how graphs enable critical evaluation of traditional confounding criteria, exposing shortcomings when multiple confounders are considered together. It then presents modifications to extend those criteria to multiconfounder settings, and introduces a DAG-based causal diagram framework closely related to the G-computation approach.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/causal-diagrams-for-epidemiologic-research/665387/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/causal-diagrams-for-epidemiologic-research/665387.png","ImageObject",300,407,{"name":92,"@type":93},"iuabcfuw","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-29",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How do causal diagrams help in epidemiologic research?","Question",{"text":112,"@type":113},"They help identify variables that must be measured and controlled to estimate unconfounded effects under stated assumptions. They also provide a structured way to summarize causal assumptions using graphs or diagrams.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What problem do the text’s authors highlight about traditional confounding criteria?",{"text":117,"@type":113},"Traditional criteria can be inadequate when multiple potential confounders must be considered simultaneously. The text explains that this reveals shortcomings not previously noticed when applying those criteria to multiconfounder situations.",{"name":119,"@type":110,"acceptedAnswer":120},"What is the role of DAG-based causal diagrams described in the text?",{"text":121,"@type":113},"The paper provides a brief introduction to causal diagram theory based on directed acyclic graphs. It emphasizes the diagrams’ logical rigor for summarizing assumptions and guiding variable selection for valid effect estimates.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},665387,1793302017,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":41},3985747874027,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","# Causal Diagrams for Epidemiologic Research\n\nSander Greenland,¹Judea Pearl,and James M.Robins³  \nCausal diagrams have a long history of informal use and,morerecently,have undergone formal development for applicationsin expert systems and robotics.We provide an introduction tothese developments and their use in epidemiologic research.Causal diagrams can provide a starting point for identifyingvariables that must be measured and controlled to obtainunconfounded effect estimates.They also provide a method for  \ncritical evaluation of traditional epidemiologic criteria for con-founding.In particular,they reveal certain heretofore unno-ticed shortcomings of those criteria when used in consideringmultiple potential confounders.We show how to modify thetraditional criteria to correct those shortcomings.(Epidemiology 1999;10:37-48)  \nKeywords:bias,causation,confounding,epidemiologic methods,graphical methods,observational studies.  \nSummarization of causal links via graphs or diagrams haslong been used as an informal aid to causal analysis.Causal graphs in the form of path diagrams are anintegral component of path analysis¹and structuralequations modeling.²In more recent times,the theory ofdirected acyclic graphs(DAGs)has been extended toapplication in expert-systems research.34 In these appli-cations,there is a pressing need for valid formal rulesthat allow an automated system or robot to deducecorrectly the presence or absence of causal links givencorrect background information and new data.The out-growth of this research has been the development of aformal theory for evaluating causal effects using thelanguage of causal diagrams.5,6 Unlike path analysis andstructural-equations modeling,this theory does not re-quire parametric assumptions such as linearity.  \nThe theory of causal graphs is equivalent to the Gcomputation theory of Robins.7-9 It has a benefit,how-ever,of providing a compact graphical as well as alge-braic formulation of assumptions and results,which maybe easier for the general reader to comprehend.In addition,it provides a novel perspective on traditionalepidemiologic criteria for confounder identification.This perspective reveals how traditional criteria can beinadequate when multiple confounders must be consid-ered simultaneously.We describe the modifications re-quired of traditional criteria that enable their valid ex-tension to situations involving multiple confounders.  \nWe here provide a brief introduction to the theory ofcausal diagrams based on DAGs.56 We pay special at-tention to its relation to nongraphical epidemiologictreatments of confounding.10-13We show how diagramscan serve as a visual yet logically rigorous aid for sum-marizing assumptions about a problem and for identify-ing variables that must be measured and controlled toobtain unconfounded effect estimates given those as-sumptions.Thus,use of such graphs can aid in planningof data collection and analysis,in communication ofresults,and in avoiding subtle pitfalls of confounderselection.  \nExcept where noted otherwise,the present paper willdeal only with relations among variables in a givensource population;that is,we will deal only with struc-tural(systematic)relations among the underlying vari-ables of interest,so that issues of measurement error andrandom variation willnot arise.We willalso not presentproofs of results,but we will give references in whichproofs can be found.  \n## A Rationale for Graphs\n\nAny deduction about a causal relation must start fromsome set of assumptions,which we call the analysismodel.¹4 For example,such a deduction may assume thatuncontrolled confounding is negligible;this assumptionusually corresponds to a set of assumptions that variousuncontrolled factors have negligible associations withthe study factor or study disease,given what has beencontrolled.The subsets of these assumptions pertainingto causation,measurement,and selection correspond tothe causal,measurement,and selection models,whereasthe subset of assumpt","cbCaiiNBLNSasveT","https://ap.wps.com/l/cbCaiiNBLNSasveT","pdf",5607265,12,"English","# A Rationale for Graphs","[{\"question\":\"How do causal diagrams help in epidemiologic research?\",\"answer\":\"They help identify variables that must be measured and controlled to estimate unconfounded effects under stated assumptions. They also provide a structured way to summarize causal assumptions using graphs or diagrams.\"},{\"question\":\"What problem do the text’s authors highlight about traditional confounding criteria?\",\"answer\":\"Traditional criteria can be inadequate when multiple potential confounders must be considered simultaneously. The text explains that this reveals shortcomings not previously noticed when applying those criteria to multiconfounder situations.\"},{\"question\":\"What is the role of DAG-based causal diagrams described in the text?\",\"answer\":\"The paper provides a brief introduction to causal diagram theory based on directed acyclic graphs. It emphasizes the diagrams’ logical rigor for summarizing assumptions and guiding variable selection for valid effect estimates.\"}]","Causal Diagrams for Epidemiologic Research | PDF"]