[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123831-en":3,"doc-seo-123831-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123831,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Flexible Machine Learning Estimation of Conditional Average Treatment Effects - A Blessing and a Curse","Causal inference from observational data depends on untestable identification assumptions. When these assumptions hold, machine learning can estimate complex heterogeneity in causal effects, including the conditional average treatment effect (ATE). If available features fail to explain all heterogeneity, individual treatment effects may deviate substantially from the conditional ATE. Using a causal random forest, this work shows how the distributions can differ and extends the method to estimate conditional variance differences between treated and controls.","Flexible Machine Learning Estimation of Conditional Average Treatment Effects  \nCitation for published version (APA):  \nPost, R. A. J. , Petkovic, M. , van den Heuvel, I. L. , & van den Heuvel, E. R. (2024) . Flexible Machine Learning Estimation of Conditional Average Treatment Effects: A Blessing and a Curse. Epidemiology , 35(1), 32-40.  \n[https://doi.org/10.1097/EDE.0000000000001684](https://doi.org/10.1097/EDE.0000000000001684)  \nDocument license:  \nTAVERNE  \nDOI:  \n10.1097/EDE.0000000000001684  \nDocument status and date:  \nPublished: 01/01/2024  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.  \n• The final author version and the galley proof are versions of the publication after peer review.  \n• The final published version features the final layout of the paper including the volume, issue and page numbers.  \nLink to publication  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal.  \nIf the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement:  \n[www.tue.nl/taverne](www.tue.nl/taverne)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[openaccess@tue.nl](openaccess@tue.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 14. Jul. 2024  \nFlexible Machine Learning Estimation of Conditional  \nAverage Treatment Effects  \nA Blessing and a Curse  \nRichard A. J. Post,a Marko Petkovic,a Isabel L. van den Heuvel,a and Edwin R. van den Heuvela,b  \nAbstract: Causal inference from observational data requires untestable identification assumptions. If these assumptions apply, machine learning methods can be used to study complex forms of causal effect heterogeneity. Recently, several machine learning methods were developed to estimate the conditional average treatment effect (ATE) . If the features at hand cannot explain all heterogeneity, the individual treatment effects can seriously deviate from the conditional ATE. In this work, we demonstrate how the distributions ofthe individual treatment effect and the conditional ATE can differ when a causal random forest is applied. We extend the causal random forest to estimate the difference in conditional variance between treated and controls. If the distribution of the individual treatment effect equals that of the conditional ATE, this estimated difference in variance should be small. If they differ, an additional causal assumption is necessary to quantify the heterogeneity not captured by the distribution of the conditional ATE. The conditional variance of the individual treatment effect can be identified when the individual effect is independent of the outcome under no treatment given the measured features. Then, in the cases where the individual treatment effect and conditionalATE distributions differ, the extended causal random forest can appropriate","cbCaibxlyb9GACYn","https://ap.wps.com/l/cbCaibxlyb9GACYn","pdf",1131157,1,10,"English","en",105,"# Abstract\n## Conditional ATE and individual treatment effect distributions\n## Causal random forest extension for conditional variance","[{\"question\":\"Why do causal inference methods require untestable identification assumptions in observational data?\",\"answer\":\"Because valid causal conclusions rely on assumptions that cannot be directly verified from the observed data alone.\"},{\"question\":\"When can individual treatment effects deviate from the conditional average treatment effect (ATE)?\",\"answer\":\"When the measured features cannot explain all causal effect heterogeneity.\"},{\"question\":\"How does the extended causal random forest help in this setting?\",\"answer\":\"It estimates the difference in conditional variance between treated and controls, and can quantify variance of the individual treatment effect distribution when causal random forest alone fails.\"}]","Flexible Machine Learning Estimation of Conditional Average Treatment Effects - A Blessing and a Curse | PDF",1785818781,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"flexible-machine-learning-estimation-of-conditional-average-treatment-effects-a-blessing-and-a-curse","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/flexible-machine-learning-estimation-of-conditional-average-treatment-effects-a-blessing-and-a-curse/123831/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do causal inference methods require untestable identification assumptions in observational data?","Question",{"text":75,"@type":76},"Because valid causal conclusions rely on assumptions that cannot be directly verified from the observed data alone.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"When can individual treatment effects deviate from the conditional average treatment effect (ATE)?",{"text":80,"@type":76},"When the measured features cannot explain all causal effect heterogeneity.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the extended causal random forest help in this setting?",{"text":84,"@type":76},"It estimates the difference in conditional variance between treated and controls, and can quantify variance of the individual treatment effect distribution when causal random forest alone fails.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]