[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118001-en":3,"doc-seo-118001-105":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},118001,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","The value added of machine learning to causal inference - evidence from revisited studies","A fast-growing econometric literature investigates how machine learning can improve causal inference, yet empirical economics has not fully leveraged these methods’ strengths. The paper revisits influential empirical studies using causal machine learning approaches and compares their results with traditional estimators. It focuses on double/debiased machine learning, causal forests, and generic machine learning for both average and heterogeneous treatment effects, illustrating implementation across settings and supporting findings with Monte Carlo simulations.","Econometrics Journal (2024), volume 00, pp. 1–22.  \n[https://doi.org/10.1093/ectj/utae004](https://doi.org/10.1093/ectj/utae004)  \nThe value added of machine learning to causal inference: evidence  \nfrom revisited studies  \nANNA BAIARDI† AND ANDREA A. NAGHI†,‡  \n†Erasmus University Rotterdam and Tinbergen Institute, Burgemeester Oudlaan 50, 3062 PA  \nRotterdam, Netherlands.  \n[Email: baiardi@ese.eur.nl](Email: baiardi@ese.eur.nl)  \n‡ Queen Mary University of London, Mile End Road, London E1 4NS, UK.  \nEmail: [a.naghi@qmul.ac.uk](a.naghi@qmul.ac.uk)  \nFirst version received: 15 September 2022; final version accepted: 13 December 2022.  \nSummary: A new and rapidly growing econometric literature is making advances in the problem of using machine learning methods for causal inference questions. Yet, the empirical economics literature has not started to fully exploit the strengths of these modern methods. Werevisit influential empirical studies with causal machine learning methods aiming to connect the econometric theory on these methods with empirical economics. We focus on the double machine learning, causal forest, and generic machine learning methods, in the context of both average and heterogeneous treatment effects. We illustrate the implementation of these methods in a variety of settings and highlight the relevance and value added relative to traditional methods used in the original studies.  \nKeywords: Average treatment effects, causal inference, heterogeneous treatment effects, machine learning.  \nJEL codes: C01, C21, D04 .  \n1. INTRODUCTION  \nOne of the key goals of empirical research in economics is to estimate the causal effect of a variable of interest on a targeted outcome. To avoid biases in the coefficients of interest due to omitted variables, particularly in observational studies, it is often desirable to include in the regressions a large number of controls. Even if the number of raw covariates is relatively small, including interactions and transformations can quickly increase the number of controls in the regression.  \nMachine learning (ML) methods can potentially be useful in such settings. However, standard ML prediction models are aimed at fundamentally different problems than most of the empirical work in economics. ML methods are designed and optimized for predicting the outcome in a test sample. Thus, a model is selected by optimizing the goodness of fit on the held-out test set. In contrast, in empirical economic research, the goodness of fit of a model is oftentimes reduced when estimating a causal effect, and the predictive accuracy is sacrificed in order to learn more deeply about a fundamental relationship that can guide policy decisions and counterfactual predictions (Athey and Imbens, 2019) . These fundamental differences will eventually generate  \n© The Author(s) 2024. Published by Oxford University Press on behalf of Royal Economic Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.  \nDownloaded from [https://academic.oup.com/ectj/advance-article/doi/10.1093/ectj/utae004/7602388 by Queen Mary College and Westfield College user on](https://academic.oup.com/ectj/advance-article/doi/10.1093/ectj/utae004/7602388 by Queen Mary College and Westfield College user on) 13 March 2024  \n2 A. Baiardi andA. A. Naghi  \nbiased estimates if standard ML techniques, designed for prediction, are used in the context of causal inference.1 Nevertheless, a new and rapidly growing econometric literature is making advances in the problem of using ML methods for causal inference questions (see, e.g., Atheyet al., 2018 ; Chernozhukov, Chetverikov et al., 2018 ; Chernozhukov, Demirer et al., 2018 ; Wagerand Athey, 2018) . This literature brings in new ","cbCaicu8hggjBs5B","https://ap.wps.com/l/cbCaicu8hggjBs5B","pdf",713570,1,22,"English","en",105,"# Introduction\n## Causal inference goals in empirical economics\n## Limits of standard ML prediction models\n## Paper aims and scope\n## Methods for ATE and HTE","[{\"question\":\"What is the main goal of the paper?\",\"answer\":\"To provide evidence from revisited empirical studies on the merits of causal machine learning methods in realistic settings and compare them with traditional approaches.\"},{\"question\":\"Which causal ML methods are emphasized?\",\"answer\":\"The paper highlights double/debiased machine learning (DML) for average treatment effects, causal forests and a generic ML approach for heterogeneous treatment effects, with strong theoretical foundations.\"},{\"question\":\"How does the paper evaluate performance beyond real-study applications?\",\"answer\":\"It supports key findings with Monte Carlo simulations where the true data-generating process is known, enabling comparisons of finite-sample performance between causal ML estimators and traditional estimators.\"}]","The value added of machine learning to causal inference - evidence from revisited studies | PDF",1785680698,55,{"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-value-added-of-machine-learning-to-causal-inference-evidence-from-revisited-studies","",{"@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-value-added-of-machine-learning-to-causal-inference-evidence-from-revisited-studies/118001/",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 is the main goal of the paper?","Question",{"text":75,"@type":76},"To provide evidence from revisited empirical studies on the merits of causal machine learning methods in realistic settings and compare them with traditional approaches.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which causal ML methods are emphasized?",{"text":80,"@type":76},"The paper highlights double/debiased machine learning (DML) for average treatment effects, causal forests and a generic ML approach for heterogeneous treatment effects, with strong theoretical foundations.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the paper evaluate performance beyond real-study applications?",{"text":84,"@type":76},"It supports key findings with Monte Carlo simulations where the true data-generating process is known, enabling comparisons of finite-sample performance between causal ML estimators and traditional estimators.","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"]