[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119146-en":3,"doc-seo-119146-105":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},119146,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Model Averaging and Double Machine Learning - IZA Discussion Paper 16714 - Abstract","Model Averaging and Double Machine Learning presents a method that combines double/debiased machine learning (DDML) with stacking to estimate structural parameters. Two new stacking variants for DDML are developed: short-stacking, which uses DDML cross-fitting to lower computational demands, and pooled stacking, which enforces shared stacking weights across cross-fitting folds. Calibrated simulations and empirical applications on gender gaps in citations and wages show improved robustness when functional forms are only partially known. Stata and R implementations are provided for practical use.","Ahrens, Achim; Hansen, Christian B.; Schaffer, Mark E; Wiemann, Thomas  \nWorking Paper  \nModel Averaging and Double Machine Learning  \nIZA Discussion Papers, No. 16714  \nProvided in Cooperation with:  \nIZA – Institute of Labor Economics  \nSuggested Citation: Ahrens, Achim; Hansen, Christian B.; Schaffer, Mark E; Wiemann, Thomas (2024) : Model Averaging and Double Machine Learning, IZA Discussion Papers, No. 16714, Institute of Labor Economics (IZA), Bonn  \nThis Version is available at:  \n[https://hdl.handle.net/10419/282841](https://hdl.handle.net/10419/282841)  \nStandard-Nutzungsbedingungen:  \nDie Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.  \nSie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.  \nSofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.  \nTerms of use:  \nDocuments in EconStor maybe saved and copied foryour personal and scholarly purposes.  \nYou are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public.  \nIf the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence.  \nDISCUSSION PAPER SERIES  \nIZA DP No. 16714  \nModel Averaging and Double Machine Learning  \nAchim Ahrens Christian B. Hansen Mark E. Schaffer Thomas Wiemann  \nJANUARY 2024  \n| DISCUSSION PAPER SERIES\u003Cbr>IZA DP No. 16714\u003Cbr>Model Averaging and Double Machine Learning |  |\n| --- | --- |\n| Achim Ahrens\u003Cbr>ETH Zürich\u003Cbr>Christian B. Hansen\u003Cbr>University of Chicago | Mark E. Schaffer\u003Cbr>Heriot-Watt University and IZA\u003Cbr>Thomas Wiemann\u003Cbr>University of Chicago |\n| JANUARY 2024 |  |\n\nAny opinions expressed in this paper are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but IZA takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity.  \nThe IZA Institute of Labor Economics is an independent economic research institute that conducts research in labor economics and offers evidence-based policy advice on labor market issues. Supported by the Deutsche Post Foundation, IZA runs the world’s largest network of economists, whose research aims to provide answers to the global labor market challenges of our time. Our key objective is to build bridges between academic research, policymakers and society.  \nIZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author.  \nISSN: 2365-9793  \nIZA – Institute of Labor Economics  \n\n| Schaumburg-Lippe-Straße 5–9 53113 Bonn, Germany | Phone: +49-228-3894-0\u003Cbr>Email: [publications@iza.org](publications@iza.org) | [www.iza.org](www.iza.org) |\n| --- | --- | --- |\n\nIZA DP No. 16714 JANUARY 2024  \nABSTRACT  \nModel Averaging and Double Machine Learning*  \nThis paper discusses pairing double/debiased machine learning (DDML) with stacking, a model averaging method for combining multiple candidate learners, to estimate structural parameters. We introduce two new stacking approaches for DDML: short-stacking exploits the cross-fitting step of DDML to substantially reduce the computational burden and pooled stacking enforces common stacking weights over cross-fitting folds. Using calibrated simulation studies and two applications estimating gender gaps in citations and wages, we show that DDML with","cbCaia0YCbzZz7GU","https://ap.wps.com/l/cbCaia0YCbzZz7GU","pdf",3560141,1,55,"English","en",105,"# Abstract\n## Introduction\n## JEL Classification and Keywords\n## Acknowledgements","[{\"question\":\"What is the core contribution of this paper?\",\"answer\":\"The paper pairs double/debiased machine learning (DDML) with stacking (a model averaging technique) to estimate structural parameters.\"},{\"question\":\"What are the two proposed stacking approaches for DDML?\",\"answer\":\"It introduces short-stacking, leveraging DDML cross-fitting to reduce computation, and pooled stacking, which imposes common stacking weights over cross-fitting folds.\"},{\"question\":\"How do the authors evaluate the method?\",\"answer\":\"They use calibrated simulation studies and applications estimating gender gaps in citations and wages.\"}]","Model Averaging and Double Machine Learning - IZA Discussion Paper 16714 - Abstract | PDF",1785722702,139,{"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},"model-averaging-and-double-machine-learning-iza-discussion-paper-16714-abstract","",{"@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/model-averaging-and-double-machine-learning-iza-discussion-paper-16714-abstract/119146/",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","2026-08-03",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 core contribution of this paper?","Question",{"text":75,"@type":76},"The paper pairs double/debiased machine learning (DDML) with stacking (a model averaging technique) to estimate structural parameters.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What are the two proposed stacking approaches for DDML?",{"text":80,"@type":76},"It introduces short-stacking, leveraging DDML cross-fitting to reduce computation, and pooled stacking, which imposes common stacking weights over cross-fitting folds.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the authors evaluate the method?",{"text":84,"@type":76},"They use calibrated simulation studies and applications estimating gender gaps in citations and wages.","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"]