[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118250-en":3,"doc-seo-118250-105":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},118250,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Estimating Causal Effects with Double Machine Learning - A Method Evaluation","Causal effect estimation using observational data remains a central research challenge because identification requires assumptions that are often strong and not directly testable. This paper reviews double/debiased machine learning (DML) and evaluates it empirically on simulated data against more traditional statistical approaches, then applies it to real-world data. Results show that employing sufficiently flexible machine learning within DML improves adjustment for nonlinear confounding and helps relax traditional functional form assumptions. The method still depends critically on causal structure and identification assumptions, and the housing-price application finds DML estimates consistently larger than less flexible methods. Actionable guidance is provided for key implementation choices.","arXiv :2403 . 14385v2 [ stat .ML] 30 Apr 2024  \nEstimating Causal Effects with Double Machine Learning-A  \nMethod Evaluation  \nJonathan Fuhr 1 , Philipp Berens2 , and Dominik Papies 1  \n1 School of Business and Economics, University of T¨ubingen, T¨ubingen, Germany  \n2 Hertie Institute for AI in Brain Health, University of T¨ubingen, T¨ubingen, Germany  \nLast edited: May 1, 2024  \nAbstract  \nThe estimation of causal effects with observational data continues to be a very active research area. In recent years, researchers have developed new frameworks which use machine learning to relax classical assumptions necessary for the estimation of causal effects. In this paper, wereview one of the most prominent methods- “double/debiased machine learning”(DML) -and empirically evaluate it by comparing its performance on simulated data relative to more traditional statistical methods, before applying it to real-world data. Our findings indicate that the application of a suitably flexible machine learning algorithm within DML improves the adjustment for various nonlinear confounding relationships. This advantage enables a departure from traditional functional form assumptions typically necessary in causal effect estimation. However, we demonstrate that the method continues to critically depend on standard assumptions about causal structure and identification. When estimating the effects of air pollution on housing prices in our application, we find that DML estimates are consistently larger than estimates of less flexible methods. From our overall results, we provide actionable recommendations for specific choices researchers must make when applying DML in practice.  \n1 Introduction  \nIn many scientific disciplines, researchers attempt to estimate causal effects (Imbens and Rubin, 2015), i.e., they ask how one variable of interest (the “treatment”) causally affects another variable (the “outcome”) . For example, labor economists care about the effect of education on wages (e.g. , Card, 1999), physicians want to know whether smoking causes lung cancer (e.g., Cornfield et al. , 2009), and marketing managers want to know how a price change affects demand (e.g., Bijmolt et al., 2005) . For many of these questions, researchers have to rely on observational data because experimental interventions may be infeasible, unethical, or simply too costly to obtain (e.g., Athey and Imbens, 2017) . However, without experimental variation, identifying and estimating causal effects is not possible without making assumptions. These assumptions can be strong and are generally not testable in observational studies (e.g., Pearl, 2009; Imbens and Rubin, 2015) . For example, a typical assumption is that all variables affecting both the treatment and the outcome variable (so-called “confounders”) are observed and adequately adjusted for (e.g., Imbens, 2004) . Justifying the necessary assumptions is one of the biggest challenges in real-world applications of causal inference (e.g., Hern´an and Robins, 2020) . As a consequence, researchers are interested in developing and applying methods that work under weaker or more plausible assumptions.  \nIndeed, researchers have recently suggested that some of the necessary assumptions can be relaxed by relying on new causal inference frameworks based on machine learning. Traditionally,(supervised) machine learning (ML) has established itself as a powerful tool for making predictions in complex, nonlinear settings. ML methods are also capable of handling high-dimensional data, i.e., data where the number of variables or parameters may even exceed the number of observations (see, e.g., Hastie et al., 2009) . However, the goal of achieving high predictive accuracy is fundamentally different from the typical goal of causal inference, which is accurate parameter or effect estimation (Shmueli, 2010; Mullainathan and Spiess, 2017) . A consequence of these different goals is that directly using ML methods designed for prediction “of","cbCaip3AeMDwkP27","https://ap.wps.com/l/cbCaip3AeMDwkP27","pdf",2535326,1,67,"English","en",105,"# Introduction\n## Causal effect estimation challenges with observational data\n## Machine learning as a tool for causal inference\n## Double/debiased machine learning (DML) overview","[{\"question\":\"What problem does this paper address in causal inference?\",\"answer\":\"It addresses how to estimate causal effects from observational data, where identification typically relies on strong, hard-to-test assumptions and missing or misspecified confounding adjustment can bias results.\"},{\"question\":\"How does double/debiased machine learning (DML) aim to improve causal effect estimation?\",\"answer\":\"DML uses flexible machine learning to adjust for observed confounding, reducing reliance on restrictive parametric functional-form assumptions and improving handling of nonlinear confounding relationships.\"},{\"question\":\"What do the simulation and real-world results suggest about DML performance?\",\"answer\":\"On simulated data, DML is evaluated against traditional methods to compare performance, and in the real-world application to air pollution and housing prices, DML estimates are consistently larger than estimates from less flexible methods.\"}]","Estimating Causal Effects with Double Machine Learning - A Method Evaluation | PDF",1785682638,169,{"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},"estimating-causal-effects-with-double-machine-learning-a-method-evaluation","",{"@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/estimating-causal-effects-with-double-machine-learning-a-method-evaluation/118250/",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","2026-08-02",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 problem does this paper address in causal inference?","Question",{"text":75,"@type":76},"It addresses how to estimate causal effects from observational data, where identification typically relies on strong, hard-to-test assumptions and missing or misspecified confounding adjustment can bias results.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does double/debiased machine learning (DML) aim to improve causal effect estimation?",{"text":80,"@type":76},"DML uses flexible machine learning to adjust for observed confounding, reducing reliance on restrictive parametric functional-form assumptions and improving handling of nonlinear confounding relationships.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the simulation and real-world results suggest about DML performance?",{"text":84,"@type":76},"On simulated data, DML is evaluated against traditional methods to compare performance, and in the real-world application to air pollution and housing prices, DML estimates are consistently larger than estimates from less flexible methods.","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"]