[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-665389-105":59,"doc-detail-665389-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-further-readings","Causal Diagrams - Further Readings","","Causal Diagrams provides an overview of how causal systems models, including structural-equations models, use graphical or path diagrams to summarize qualitative assumptions. It explains how such diagrams gained formal probability interpretations by the 1980s, enabling probabilistic inference and derivations of implied independence conditions. It also describes how, by the 1990s, the graphs support causal inference tasks such as estimating target causal effects and representing them as standardized measures, supported by further readings.",{"@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-further-readings/665389/",{"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-further-readings/665389.png","ImageObject",300,407,{"name":92,"@type":93},"Guten Appetit_Joghurt","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-29",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the role of causal diagrams in structural-equations models?","Question",{"text":112,"@type":113},"Causal diagrams accompany causal systems models by providing graphical representations that summarize qualitative assumptions. They offer a compact visual way to encode relationships among variables.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How did the interpretation of these diagrams change over time?",{"text":117,"@type":113},"By the 1980s, the diagrams were recognized as probability models, enabling formal probabilistic inference. By the 1990s, they were also used as a formal tool for causal inference, including intervention-based effect estimation.",{"name":119,"@type":110,"acceptedAnswer":120},"What graph-theory terms are used to describe relationships among variables?",{"text":121,"@type":113},"Variables are represented as nodes or vertices, and connections are arcs, edges, or links. Arrows define parent (tail) and child (head) variables, while terminology such as exogenous/endogenous and sink/source nodes describes how variables relate within the graph.","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},665389,1793302021,{"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":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":46},1374408768420,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Causal Diagrams 149  \nsection, a regional difference in practice patterns in this group will be most noticeable. In this example, it is clear that such an effect is evident. For the study at hand, there is no ‘‘correct’’ choice. It may bea study where Table 11 is provided. A smaller summary may be prepared as in Table 12 . With only one anomaly in combining ages 18 to 49 years within the other two factors, the overall story appears reasonable to combine these ages. Collapsing the estimate by insurance type may also be useful.  \nTo summarize, many decisions need to be made during the statistical analysis of epidemiologic data. Maintaining a focus on the research question of interest is important and more difficult for those with little experience. Understanding the nature of the association, including how the exposure may be related to the outcome, even if only theoretically, can aid the decision-making process.  \n—Robert Bednarczyk and Louise-Anne McNutt  \nSee also Causal Diagrams; Causation and Causal Inference; Confounding; Effect Modification and Interaction; Study Design  \nFurther Readings  \nAgresti, A. (2002) . Categorical data analysis (2nd ed.) . Hoboken, NJ: Wiley-Interscience.  \nAgresti, A. (2007) . An introduction to categorical data analysis (2nd ed.) . Hoboken, NJ: Wiley-Interscience.  \nDaniel, W. W. (1999) . Biostatistics: A foundation for analysis in the health sciences. Hoboken, NJ: Wiley.  \nGoetz, M. A. (2000) . Think outside the box: Analysis of categorical data. Presented at 2000 Northeast SAS Users Group, Inc. Meeting. Retrieved June 28, 2007, from [http://nesug.org/proceedings/nesug00/ps/ps7008.pdf](http://nesug.org/proceedings/nesug00/ps/ps7008.pdf).  \nGordis, L. (2004) . Epidemiology (3rd ed.) . Philadelphia: Elsevier Saunders.  \nHennekens, C. H., Buring, J. E., & Mayrent, S. L. (1987) . Epidemiology in medicine. Boston: Little, Brown. Mantel, N., & Haenszel, W. (1959) . Statistical aspects of  \nthe analysis of data from retrospective studies of disease. Journal of the National Cancer Institute, 22(4), 719–748.  \nRothman, K. J. (2002) . Epidemiology: An introduction. Oxford, UK: Oxford University Press.  \nRothman, K. J, & Greenland, S. (1998) . Modern epidemiology (2nd ed.) . Philadelphia: Lippincott Williams & Wilkins.  \nStokes, M. E., Davis, C. S., & Koch, G. G. (2000) . Categorical data analysis using the SAS system (2nd ed.) . Cary, NC: SAS Institute.  \nSzklo, M., & Nieto, F. J. (2004) . Epidemiology: Beyond the basics. Sudbury, MA: Jones & Bartlett.  \n\n| CAUSAL DIAGRAMS |  |  |  |  |\n| --- | --- | --- | --- | --- |\n| From their inception in the early 20th century, causal systems models (more commonly known as structural-equations models) were accompanied by graphical representations or path diagrams that provided compact summaries of qualitative assumptions made by the models. Figure 1 provides a graph that would correspond to any system of five equations encoding these assumptions:\u003Cbr>1. Independence of A and B\u003Cbr>2. Direct dependence of C on A and B\u003Cbr>3. Direct dependence of E on A and C\u003Cbr>4. Direct dependence of F on C\u003Cbr>5. Direct dependence of D on B, C, and E\u003Cbr>The interpretation of ‘‘direct dependence’’ was kept rather informal and usually conveyed by causal intuition, for example, that the entire influence of A on Fis ‘‘mediated’’ by C.\u003Cbr>By the 1980s, it was recognized that these diagrams could be reinterpreted formally as probability models, which opened the visual power of graph theory for use in probabilistic inference and allowed easy deduction of other independence conditions implied by the assumptions. By the 1990s, it was further recognized that these diagrams could also be\u003Cbr>A B |  |  |  |  |\n|  | | C\u003Cbr>\u003Cbr>F | |  |\n| E  D |  |  |  |  |\n\nFigure 1 Example of a Directed ~~Acrylic~~ Graph  \n150 Causal Diagrams  \nused as a formal tool for causal inference, such as predicting the effects of external interventions. Given that the graph is correct, one can see whether the causal effects o","cbCaiinQInQhDyaV","https://ap.wps.com/l/cbCaiinQInQhDyaV","pdf",694863,"English","# Causal Diagrams\n## Further Readings\n## Basics of Graph Theory","[{\"question\":\"What is the role of causal diagrams in structural-equations models?\",\"answer\":\"Causal diagrams accompany causal systems models by providing graphical representations that summarize qualitative assumptions. They offer a compact visual way to encode relationships among variables.\"},{\"question\":\"How did the interpretation of these diagrams change over time?\",\"answer\":\"By the 1980s, the diagrams were recognized as probability models, enabling formal probabilistic inference. By the 1990s, they were also used as a formal tool for causal inference, including intervention-based effect estimation.\"},{\"question\":\"What graph-theory terms are used to describe relationships among variables?\",\"answer\":\"Variables are represented as nodes or vertices, and connections are arcs, edges, or links. Arrows define parent (tail) and child (head) variables, while terminology such as exogenous/endogenous and sink/source nodes describes how variables relate within the graph.\"}]","Causal Diagrams - Further Readings | PDF"]