[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120608-en":3,"doc-seo-120608-105":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},120608,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Machine Learning Debiasing with Conditional Moment Restrictions - An Application to LATE","Models with Conditional Moment Restrictions (CMRs) are widely used in economics, combining finite- and infinite-dimensional components such as conditional expectations, choice probabilities, and policy functions that can be flexibly estimated using machine learning. This paper characterizes locally debiased moments for regular semiparametric CMR models with potentially different conditioning variables, establishing existence and relevance and enabling standard √n-consistent, asymptotically normal inference. As an application, it develops debiased machine learning for LATE with endogeneity, proposes the Compliance Machine Learning Estimator (CML), and validates performance numerically, also revisiting the Oregon Health Insurance Experiment.","Machine Learning Debiasing with Conditional Moment Restrictions:  \nAn Application to LATE ∗  \narXiv :2410 .23785v1 [ econ .EM] 31 Oct 2024  \nFacundo Arga˜naraz†  \nUniversidad Carlos III de Madrid  \nJuan Carlos Escanciano‡ Universidad Carlos III de Madrid  \nNovember 1, 2024  \nAbstract  \nModels with Conditional Moment Restrictions (CMRs) are popular in economics. These models involve finite and infinite dimensional parameters. The infinite dimensional components include conditional expectations, conditional choice probabilities, or policy functions, which might be flexibly estimated using Machine Learning tools. This paper presents a characterization of locally debiased moments for regular models defined by general semiparametric CMRs with possibly different conditioning variables. These moments are appealing as they are known to be less affected by first-step bias. Additionally, we study their existence and relevance. Such results apply to a broad class of smooth functionals of finite and infinite dimensional parameters that do not necessarily appear in the CMRs. As a leading application of our theory, we characterize debiased machine learning for settings of treatment effects with endogeneity, giving necessary and sufficient conditions. We present a large class of relevant debiased moments in this context. We then propose the Compliance Machine Learning Estimator (CML), based on a practically convenient orthogonal relevant moment. We show that the resulting estimand can be written as a convex combination of conditional local average treatment effects (LATE) . Altogether, CML enjoys three appealing properties in the LATE framework: (1) local robustness to first-stage estimation, (2) an estimand that can be identified under a minimal relevance condition, and (3) a meaningful causal interpretation. Our numerical experimentation shows satisfactory relative performance of such an estimator. Finally, we revisit the Oregon Health Insurance Experiment, analyzed by Finkelstein et al. (2012) . We find that the use of machine learning and CML suggest larger positive effects on health care utilization than previously determined.  \nKeywords: Local Treatment Effects; Debiased Inference; Machine Learning.  \nJEL classi􀀌cation: C14; C31; C36 .  \n∗An earlier version of this paper circulated under the title: “On the Existence and Information of Orthogonal Moments for Inference”, arXiv 2303.11418 . We thank Guido Imbens and participants at many institutions for useful comments. Research funded by Ministerio de Ciencia e Innovaci´on grant PID2021-127794NB-I00 and Comunidad de Madrid, grants EPUC3M11 (VPRICIT) and H2019/HUM-589 .  \n†Department of Economics. E-mail: [farganar@eco.uc3m.es. Website:](farganar@eco.uc3m.es. Website:) [https://argafacu.github.io](https://argafacu.github.io).  \n‡Department of Economics. E-mail: jescanci@econ.uc3m.es. Website: [https://sites.google.com/view/juancarlosescanciano](https://sites.google.com/view/juancarlosescanciano).  \n1 Introduction  \nModels with Conditional Moment Restrictions (CMRs) are popular in economics and statistics, appearing in regressions, quantile models, discrete choice models, demand estimation, and missing data problems, among others; see Chen and Qiu (2016) for a wide range of settings with CMRs. An important subclass subsumes semiparametric specifications, where the model depends on infinite and finite dimensional parameters. The finite dimensional parameters might be some coefficient or treatment effect, which are the focus of the analysis. The infinite dimensional components include conditional expectations, conditional choice probabilities, or policy functions, which might be flexibly estimated using Machine Learning tools, such as Lasso, Random Forest, Boosting, Neural Networks, and the like.  \nThese tools have been proved useful in undertaking flexible estimation of high-dimensional objects. They are able to deliver reliable predictions by achieving a suitable bias-variance trade-off. Thus, the estimation of these h","cbCaicrTVBPi0de0","https://ap.wps.com/l/cbCaicrTVBPi0de0","pdf",469963,1,39,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What problem does the paper address in machine-learning-based CMR models?\",\"answer\":\"First-step machine learning estimates are biased, and that bias can contaminate second-step parameters, breaking standard inference such as √n-consistency and asymptotic normality.\"},{\"question\":\"How does the paper restore valid inference?\",\"answer\":\"It characterizes locally debiased (locally robust/orthogonal) moments for regular semiparametric CMRs, yielding estimators that are √n-consistent and asymptotically normal while being less affected by first-stage bias.\"},{\"question\":\"What is the main application discussed for LATE?\",\"answer\":\"The paper develops debiased machine learning for treatment effects with endogeneity, proposes the Compliance Machine Learning Estimator (CML), and expresses the estimand as a convex combination of conditional local average treatment effects.\"}]","Machine Learning Debiasing with Conditional Moment Restrictions - An Application to LATE | PDF",1785730877,98,{"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},"machine-learning-debiasing-with-conditional-moment-restrictions-an-application-to-late","",{"@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/machine-learning-debiasing-with-conditional-moment-restrictions-an-application-to-late/120608/",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 problem does the paper address in machine-learning-based CMR models?","Question",{"text":75,"@type":76},"First-step machine learning estimates are biased, and that bias can contaminate second-step parameters, breaking standard inference such as √n-consistency and asymptotic normality.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper restore valid inference?",{"text":80,"@type":76},"It characterizes locally debiased (locally robust/orthogonal) moments for regular semiparametric CMRs, yielding estimators that are √n-consistent and asymptotically normal while being less affected by first-stage bias.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the main application discussed for LATE?",{"text":84,"@type":76},"The paper develops debiased machine learning for treatment effects with endogeneity, proposes the Compliance Machine Learning Estimator (CML), and expresses the estimand as a convex combination of conditional local average treatment effects.","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"]