[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118079-en":3,"doc-seo-118079-105":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},118079,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","The value added of machine learning to causal inference - Evidence from revisited studies","A rapidly growing econometric literature develops machine learning methods for causal inference, yet empirical economics has not fully leveraged the strengths of these modern tools. The study revisits influential applications, linking econometric theory with empirical practice. It emphasizes double machine learning, causal forests, and generic machine learning approaches, covering both average and heterogeneous treatment effects. Implementations across varied settings demonstrate the relevance and added value versus traditional methods used in the original studies.","EUR Research Information Portal  \nThe value added of machine learning to causal inference: Evidence from revisited studies  \nPublished in:  \nEconometrics Journal  \nPublication status and date:  \nPublished: 01/05/2024  \nDOI (link to publisher):  \n10.1093/ectj/utae004  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nDocument License/Available under:  \nCC BY  \nCitation for the published version (APA):  \nBaiardi, A. , & Naghi, A. (2024) . The value added of machine learning to causal inference: Evidence from revisited studies. Econometrics Journal, 27(2), 213-234 . [https://doi.org/10.1093/ectj/utae004](https://doi.org/10.1093/ectj/utae004)  \nLink to publication on the EUR Research Information Portal  \nTerms and Conditions of Use  \nExcept as permitted by the applicable copyright law, you may not reproduce or make this material available to any third party without the prior written permission from the copyright holder(s) . Copyright law allows the following uses of this material without prior permission:  \n• you may download, save and print a copy of this material for your personal use only;  \n• you may share the EUR portal link to this material.  \nIn case the material is published with an open access license (e.g. a Creative Commons (CC) license), other uses may be allowed. Please check the terms and conditions of the specific license.  \nTake-down policy  \nIf you believe that this material infringes your copyright and/or any other intellectual property rights, you may request its removal by contacting us at the following email address: [openaccess.library@eur.nl. Please](openaccess.library@eur.nl. Please) provide us with all the relevant information, including the reasons why you believe any of your rights have been infringed. In case of a legitimate complaint, we will make the material inaccessible and/or remove it from the website.  \nEconometrics Journal (2024), volume 27, pp. 213–234.  \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†Department of Economics, Erasmus University Rotterdam and Tinbergen Institute, Burgemeester Oudlaan 50, 3062 PA Rotterdam, Netherlands.  \n[Email: baiardi@ese.eur.nl](Email: baiardi@ese.eur.nl)  \n‡Business Analytics and Applied Economics, Queen Mary University of London, Mile End  \nRoad, 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 economicsisto 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 regressionsa large number of controls. 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