[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-665391-105":59,"doc-detail-665391-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","methods-in-causal-inference-part-1-causal-diagrams-and-confounding","Methods in causal inference - Part 1 - causal diagrams and confounding","","Causal inference requires contrasting counterfactual states under specified interventions, and translating those contrasts into usable results from data. This workflow depends on explicit assumptions and careful, multi-step analysis. Causal directed acyclic graphs (causal DAGs) clarify whether counterfactual contrasts are identifiable from non-experimental observational data. The tutorial explains how to use causal DAGs to determine identifiability and causal effect identification, while providing practical reporting guidance and warnings about common misuses.",{"@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/methods-in-causal-inference-part-1-causal-diagrams-and-confounding/665391/",{"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/methods-in-causal-inference-part-1-causal-diagrams-and-confounding/665391.png","ImageObject",300,407,{"name":92,"@type":93},"BAd apple","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-29",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the core idea behind causal inference in this paper?","Question",{"text":112,"@type":113},"Causal inference is based on contrasting counterfactuals for a well-defined population under different levels of intervention.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Why are causal directed acyclic graphs (DAGs) important here?",{"text":117,"@type":113},"Causal DAGs help evaluate whether and how causal effects can be identified from observational, non-experimental data by making assumptions explicit.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the paper connect confounding to causal interpretation?",{"text":121,"@type":113},"It argues that the main challenge in moving from data to causal claims is confounding, and that inadequate attempts to emulate randomisation can undermine consistent causal estimation even in experiments.","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},665391,1793302025,{"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":81,"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":144},19241458193282,"https://us-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Evolutionary Human Sciences (2024), 6, e40, page 1 of 34 doi:10.1017/ehs.2024.35  \nMETHODS PAPER  \nMethods in causal inference . Part 1: causal diagramsand confounding  \nJoseph A. Bulbulia   \nVictoria University of Wellington, Wellington, New Zealand [Email:](Email: joseph.bulbulia@vuw.ac.nz)[ joseph.bulbulia@vuw.ac.nz](Email: joseph.bulbulia@vuw.ac.nz)  \n(Received 12 June 2024; accepted 14 June 2024)  \nAbstract  \nCausal inference requires contrasting counterfactual states under specified interventions. Obtaining these contrasts from data depends on explicit assumptions and careful, multi-step workflows. Causal diagrams are crucial for clarifying the identifiability of counterfactual contrasts from data. Here, I explain how to use causal directed acyclic graphs (DAGs) to determine if and how causal effects can be identified from non-experimental observational data, offering practical reporting tips and suggestions to avoid common pitfalls.  \nKeywords: Causal inference; culture; DAGs; evolution; tutorial  \nMedia summary: Causal directed acyclic graphs (causal DAGs) are powerful tools for clarifying assumptions required for causal inference. However, they can be easily misused. This tutorial provides guidance on safely integrating causal diagrams into analytic workflows, underscoring the importance of starting with clearly defined causal questions.  \nIntroduction  \nHuman research begins with two fundamental questions:  \n1. What do I want to know?  \n2. For which population does this knowledge generalise?  \nIn the human sciences, our questions are typically causal. We aim to understand the effects of interventions on certain variables. However, many researchers collect data, apply complex regressions, and report model coefficients without understanding that the assumptions needed to support causal inferences differ from those needed to support predictions. Even when our models predict well, it typically remains unclear how these predictions relate to the scientific questions that sparked our interest.  \nSome say that association cannot imply causation and prohibit causal inferences from observational data. However, our experimental traditions reveal that when interventions are controlled and randomised, the coefficients we recover from statistical models can permit causal interpretations. The thread to causal inference is not from associations but rather from confounding. Despite familiarity with experimental protocols, however, many researchers struggle to address confounding by emulating randomisation and control using non-experimental or ‘real-world’ data. Practices of confounding control are not systematic. Indeed, we often overlook that what we take as control can inadvertently undermine our ability to consistently estimate causal effects, even in experiments (Montgomery et al., 2018) . Although the term ‘crisis’is arguably overused in the human sciences, the state of causal inference leaves considerable headroom  \n© The Author(s), 2024 . Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.  \n[https://doi.org/10.1017/ehs.2024.35](https://doi.org/10.1017/ehs.2024.35) Published online by Cambridge University Press  \n2 Joseph A. Bulbulia  \nfor improvement. ‘Headroom for improvement’ applies to poor experimental designs that unintentionally weaken causal claims (Bulbulia, 2024d; Hernán et al., 2017; Montgomery et al., 2018).  \nFortunately, recent decades have seen considerable progress in causal data science, commonly called ‘causal inference’, or ‘CI’. The progress has transformed those areas of health science, economics, political science and computer science that have adopted it. Causal inference provides methods for obtaining vali","cbCailn6dngmba5M","https://ap.wps.com/l/cbCailn6dngmba5M","pdf",5531554,34,"English","# Introduction\n## Counterfactual contrasts and population\n## Predictions vs causal inferences\n## Confounding and control\n# Causal inference foundations\n## Potential outcomes and interventions\n# Causal DAGs in the workflow\n## Graphical building blocks\n## Identification rules\n# Applications in later parts\n## Identification problems and repeated-measures data","[{\"question\":\"What is the core idea behind causal inference in this paper?\",\"answer\":\"Causal inference is based on contrasting counterfactuals for a well-defined population under different levels of intervention.\"},{\"question\":\"Why are causal directed acyclic graphs (DAGs) important here?\",\"answer\":\"Causal DAGs help evaluate whether and how causal effects can be identified from observational, non-experimental data by making assumptions explicit.\"},{\"question\":\"How does the paper connect confounding to causal interpretation?\",\"answer\":\"It argues that the main challenge in moving from data to causal claims is confounding, and that inadequate attempts to emulate randomisation can undermine consistent causal estimation even in experiments.\"}]","Methods in causal inference - Part 1 - causal diagrams and confounding | PDF",86]