[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123744-en":3,"doc-seo-123744-105":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},123744,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","Using Machine Learning to Individualize Treatment Effect Estimation - Challenges and Opportunities","Randomized clinical trials are widely used to guide treatment decisions, but their average treatment effects often fail to generalize to real-world patients with different characteristics. This review explains how machine learning can estimate conditional average treatment effects (CATE) for individual patients using observational data, potentially enabling more accurate, personalized effect predictions. It analyzes key barriers such as satisfying identification assumptions, handling covariate shift, and learning without access to the true outcome label, while outlining applications and future collaborations to improve patient benefit.","Using Machine Learning to Individualize Treatment Effect Estimation: Challengesand Opportunities  \nAlicia Curth 1,*  , Richard W. Peck2,3  , Eoin McKinney4,5  , James Weatherall6  and Mihaela van der Schaar 1,5,7   \nThe use of data from randomized clinical trials to justify treatment decisions for real-world patients is the current state of the art. It relies on the assumption that average treatment effects from the trial can be extrapolated to patients with personal and/or disease characteristics different from those treated in the trial. Yet, because of heterogeneity of treatment effects between patients and between the trial population and real-world patients, this assumption may not be correct for many patients. Using machine learning to estimate the expected conditional average treatment effect (CATE) in individual patients from observational data offers the potential for more accurate estimation of the expected treatment effects in each patient based on their observed characteristics. In this review, we discuss some of the challenges and opportunities for machine learning to estimate CATE, including ensuring identification assumptions are met, managing covariate shift, and learning without access to the true label of interest. We also discuss the potential applications as well as future work and collaborations needed to further improve identification and utilization of CATE estimates to increase patient benefit.  \nHealthcare professionals try to make treatment decisions about individual patients using the best available evidence. In such evidence-based medicine, the data informing these individual patient decisions often comes from randomized clinical trials (RCTs) in populations of patients who have the same disease asthe patient needing treatment now. The underlying assumption is that the patient needing treatment is similar to those studied in the clinical trials and will respond in a similar manner. Yet, this assumption is often not correct and there would be great benefit in having better methods to estimate expected effect in each individual patient. Such methods could be developed either during trials of novel medicines or from real-world evidence generated after approval, and – assuming they provide a net benefit – accompany those medicines as they are approved and embedded in healthcare systems around the world.  \nThe problems with clinical trial evidence  \nRCTs remain the founding cornerstone of evidence-based medicine. They routinely form the basis of clinical decision making and guide health policy development. Yet, this clinical evidence base has several fundamental limitations. RCTs constrain recruitment to a limited group of clinically homogenous patients, limiting the impact of confounding conditions but also our ability to extrapolate results to excluded patient groups. Older patients and those with comorbidities (populations that substantially overlap) are routinely excluded from RCTs,1,2 yet, they are increasingly the  \nvery population we need to treat. Staggeringly, more than half of RCTs exclude at least 75% of the potentially treatable population.3,4 Despite these limitations, the evidence generated from RCTs is routinely extrapolated to excluded patients as the only alternative is not to use the treatment in them at all.5 Sometimes, this extrapolation can be supported by approaches such as bridging studies, real-world studies, or Bayesian borrowing, although on other occasions there is no additional evidence on which to base decision making.6  \nRCTs also allocate all patients to either the intervention or the control group (receiving standard of care, a comparator drug, or placebo treatment) and seek to identify a difference in the “average” treatment effect at this group level.7 Thus, another major limitation is that they can only summarize average treatment effects across that group: they cannot estimate the individual treatment effect for any given patient. Heterogeneity of treatment effects (HT","cbCaihSxRZWoGtMx","https://ap.wps.com/l/cbCaihSxRZWoGtMx","pdf",281718,1,10,"English","en",105,"# Using machine learning for CATE estimation\n## Evidence-based medicine and trial limitations\n## Challenges: identification, covariate shift, and unobserved labels\n## Opportunities and future work","[{\"question\":\"Why do average treatment effects from randomized clinical trials often fail for individual patients?\",\"answer\":\"Trials estimate effects at the group level and assume trial participants resemble real-world patients. In practice, treatment-effect heterogeneity and differences in patient characteristics mean the average may not reflect an individual’s expected benefit.\"},{\"question\":\"What does conditional average treatment effect (CATE) aim to provide?\",\"answer\":\"CATE targets the expected treatment effect for an individual conditional on their observed characteristics, enabling more personalized treatment decisions than group-level averages.\"},{\"question\":\"What major challenges arise when learning CATE from observational data?\",\"answer\":\"Key challenges include ensuring identification assumptions are satisfied, managing covariate shift between training and target patients, and learning when the true label of interest is not directly available.\"}]","Using Machine Learning to Individualize Treatment Effect Estimation - Challenges and Opportunities | PDF",1785818297,25,{"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},"using-machine-learning-to-individualize-treatment-effect-estimation-challenges-and-opportunities","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/using-machine-learning-to-individualize-treatment-effect-estimation-challenges-and-opportunities/123744/",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 average treatment effects from randomized clinical trials often fail for individual patients?","Question",{"text":75,"@type":76},"Trials estimate effects at the group level and assume trial participants resemble real-world patients. In practice, treatment-effect heterogeneity and differences in patient characteristics mean the average may not reflect an individual’s expected benefit.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does conditional average treatment effect (CATE) aim to provide?",{"text":80,"@type":76},"CATE targets the expected treatment effect for an individual conditional on their observed characteristics, enabling more personalized treatment decisions than group-level averages.",{"name":82,"@type":73,"acceptedAnswer":83},"What major challenges arise when learning CATE from observational data?",{"text":84,"@type":76},"Key challenges include ensuring identification assumptions are satisfied, managing covariate shift between training and target patients, and learning when the true label of interest is not directly available.","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},[93,97,101,105,110,115,118,123,128,131,134],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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"]