[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127071-en":3,"doc-seo-127071-105":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},127071,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","The Prediction-Explanation Fallacy - A Pervasive Problem in Scientific Applications of Machine Learning","The paper identifies the Prediction-Explanation Fallacy as a pervasive source of distorted inferences in scientific machine learning. The fallacy arises when prediction-optimized models are used for explanatory claims without accounting for tradeoffs. Prediction-focused training can introduce deliberate bias or unrealistic assumptions to reduce overfitting, or produce complex structures that resist interpretation. Even when multiple models predict equally well, they may imply conflicting explanations, undermining conclusions and replicability. The work presents non-technical tradeoffs, neuroscience examples, and mitigation strategies.","The Prediction-Explanation Fallacy: A Pervasive Problem in Scientific Applications of Machine Learning  \nMarco Del Giudice 1   \n[1] Department of Life Sciences, University of Trieste, Trieste, Italy.  \n\n| Methodology, 2024, Vol. 20(1), 22–46, [https://doi.org/10.5964/meth.11235](https://doi.org/10.5964/meth.11235) |\n| --- |\n| Received: 2023-01-26 • Accepted: 2024-01-19 • Published (VoR): 2024-03-22 |\n| Handling Editor: Katrijn van Deun, Tilburg University, Tilburg, the Netherlands |\n| Corresponding Author: Marco Del Giudice, Department of Life Sciences, University of Trieste. Via Weiss 2, 341278\u003Cbr>Trieste, Italy. E-mail: marco.delgiudice@units.it |\n\nAbstract  \nI highlight a problem that has become ubiquitous in scientific applications of machine learning and can lead to seriously distorted inferences. I call it the Prediction-Explanation Fallacy. The fallacy occurs when researchers use prediction-optimized models for explanatory purposes, without considering the relevant tradeoffs. This is a problem for at least two reasons. First, predictionoptimized models are often deliberately biased and unrealistic in order to prevent overfitting. In other cases, they have an exceedingly complex structure that is hard or impossible to interpret. Second, different predictive models trained on the same or similar data can be biased in different ways, so that they may predict equally well but suggest conflicting explanations. Here I introduce the tradeoffs between prediction and explanation in a non-technical fashion, present illustrative examples from neuroscience, and end by discussing some mitigating factors and methods that can be used to limit the problem.  \nKeywords  \nbias-variance tradeoff, machine learning, prediction, Rashomon effect  \nIn this paper, I wish to highlight a problem that has become ubiquitous in scientific applications of machine learning (ML) methods. As far as I can tell, this problem has not yet been singled out for discussion in the literature; but it deserves to be named, clearly described, and widely understood by researchers, who are increasingly relying on ML techniques without always appreciating their limits and constraints.  \nIn a nutshell, the prediction-explanation fallacy occurs when researchers use prediction-optimized models for explanatory purposes, without considering the tradeoffs be  \nThis is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License, CC BY 4.0, which permits unrestricted use, distribution, and reproduction, provided the original work is properly cited.  \ntween prediction and explanation. This is a problem for at least two connected reasons. First, in many typical applications of ML techniques, prediction-optimized models are deliberately biased and unrealistic in order to prevent overfitting, and hence may fail to accurately explain the phenomenon of interest. In other cases, the models have an exceedingly complex structure that is hard or impossible to interpret, which greatly limits their explanatory value. Second, different predictive models trained on the same or similar data can be biased in different ways, with the result that multiple models may predict equally well but suggest conflicting, mutually inconsistent explanations of the underlying phenomenon.  \nThe tension between prediction and explanation is not a novel concept, and has been discussed a number of times in the literature on ML and its applications (e.g., Breiman, 2001; Shmueli, 2010; Yarkoni & Westfall, 2017) . However, previous authors have focused mainly on the other side of the issue—namely, the fact that “classical” statistical models designed for accurate explanation tend to perform badly in prediction tasks (for an exception, see the recent contribution by Hofman et al., 2021). My goal is to redress this imbalance, by explicitly discussing the limitations and pitfalls of predictive models when they are used in the context of scientific explanation. As ","cbCaio1myP3vRh0D","https://ap.wps.com/l/cbCaio1myP3vRh0D","pdf",845195,1,25,"English","en",105,"# Prediction ≠ Explanation\n## Methodology\n# Abstract and Key Concepts\n## Tradeoffs Between Prediction and Explanation\n## Distorted Inferences and Conflicting Explanations\n# Examples and Mitigation","[{\"question\":\"What is the Prediction-Explanation Fallacy in scientific machine learning?\",\"answer\":\"It occurs when prediction-optimized models are used for explanatory purposes without considering the tradeoffs between prediction and explanation. This can produce misleading interpretations of the phenomenon studied.\"},{\"question\":\"Why can prediction-optimized models fail as explanatory models?\",\"answer\":\"They are often deliberately biased or unrealistic to prevent overfitting, or they may be too complex to interpret. Both issues limit their explanatory value.\"},{\"question\":\"How can different predictive models lead to conflicting explanations?\",\"answer\":\"Models trained on the same or similar data can be biased in different ways. As a result, they may predict equally well while implying mutually inconsistent explanations.\"}]","The Prediction-Explanation Fallacy - A Pervasive Problem in Scientific Applications of Machine Learning | PDF",1785936668,63,{"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},"the-prediction-explanation-fallacy-a-pervasive-problem-in-scientific-applications-of-machine-learning","",{"@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/the-prediction-explanation-fallacy-a-pervasive-problem-in-scientific-applications-of-machine-learning/127071/",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","2026-08-05",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},"What is the Prediction-Explanation Fallacy in scientific machine learning?","Question",{"text":75,"@type":76},"It occurs when prediction-optimized models are used for explanatory purposes without considering the tradeoffs between prediction and explanation. This can produce misleading interpretations of the phenomenon studied.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why can prediction-optimized models fail as explanatory models?",{"text":80,"@type":76},"They are often deliberately biased or unrealistic to prevent overfitting, or they may be too complex to interpret. Both issues limit their explanatory value.",{"name":82,"@type":73,"acceptedAnswer":83},"How can different predictive models lead to conflicting explanations?",{"text":84,"@type":76},"Models trained on the same or similar data can be biased in different ways. As a result, they may predict equally well while implying mutually inconsistent explanations.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]