[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117167-en":3,"doc-seo-117167-105":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},117167,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Automatic Debiased Machine Learning via Riesz Regression","High-dimensional regressions often underpin parameters of interest such as treatment effects, policy effects, and regression decompositions, yet machine-learning estimators can be strongly biased due to regularization and model selection. This work develops debiased machine learning that uses Neyman orthogonal estimating equations to reduce first-order bias. It introduces Riesz regression estimators for Riesz representers that depend on the target parameter rather than explicit formulas, enabling end-to-end automatic debiasing with any learner, including neural nets and random forests.","arXiv :2104 . 14737v3 [math . ST] 14 Mar 2024  \nAUTOMATIC DEBIASED MACHINE LEARNING  \nVIA RIESZ REGRESSION  \nBY VICTOR CHERNOZHUKOV1,a , WHITNEY K. NEWEY1,b VÍCTOR QUINTAS-MARTÍNEZ1,c AND VASILIS SYRGKANIS2,d  \n1 Department of Economics, MIT, [a](a vchern@mit.edu)[ vchern@mit.edu](a vchern@mit.edu); [b](bwnewey@mit.edu)[wnewey@mit.edu](bwnewey@mit.edu); [c](cvquintas@mit.edu)[vquintas@mit.edu](cvquintas@mit.edu)  \n2 Department of Management Science and Engineering, Stanford University, [d](dvsyrgk@stanford.edu)[vsyrgk@stanford.edu](dvsyrgk@stanford.edu)  \nA variety of interesting parameters may depend on high dimensional regressions. Machine learning can be used to estimate such parameters. However estimators based on machine learners can be severely biased by regularization and/or model selection. Debiased machine learning uses Neyman orthogonal estimating equations to reduce such biases. Debiased machine learning generally requires estimation of unknown Riesz representers. A primary innovation of this paper is to provide Riesz regression estimators of Riesz representers that depend on the parameter of interest, rather than explicit formulae, and that can employ any machine learner, including neural nets and random forests. End-to-end algorithms emerge where the researcher chooses the parameter of interest and the machine learner and the debiasing follows automatically. Another innovation here is debiased machine learners of parameters depending on generalized regressions, including high-dimensional generalized linear models. An empirical example of automatic debiased machine learning using neural nets is given. We find in Monte Carlo examples that automatic debiasing sometimes performs better than debiasing via inverse propensity scores and never worse. Finite sample mean square error bounds for Riesz regression estimators and asymptotic theory are also given.  \n1. Introduction. Many parameters of interest depend on regressions. Examples include treatment effects, regression decompositions, and policy effects. Often, a regression may be high dimensional, depending on many variables. For example there may be many covariates for treatment effects. Machine learning methods such as neural nets, random forests, and Lasso can be used to estimate parameters of interest that depend on high dimensional regressions.  \nA general problem with estimating parameters of interest using machine learning is that machine learners are biased by regularization and/or model selection. This bias may pass through when the learner is plugged into a formula for a parameter of interest and make the parameter estimator highly biased. This problem can be avoided by using Neyman orthogonal estimating equations where machine learners have zero first-order effect. Cross-fitting, a form of sample splitting, can also help.  \nThe orthogonal estimating equations for regressions depend on a Riesz representer α0 that must be estimated. The primary innovation of this paper is to provide an automatic estimator of α0 that uses only the definition of the parameter of interest and the regression but does not require knowing a formula for α0. We give an objective function with expectation that is minimized at α0 that depends only the parameter of interest. We refer to minimization of this objective function as a Riesz regression, being equivalent to minimizing the expected squared deviation from α0. Neural nets, random forests, and other methods can be used for  \nMSC2020 subject classifications: Primary 62D20, 62P20; secondary 62G20, 62J02 .  \nKeywords and phrases: Debiased Machine Learning, Generalized Linear Models, Riesz Representers, Neural Nets.  \n2  \nthis Riesz regression. Using the Riesz regression estimator in the bias correction completesan algorithm that 1) specifies the parameter of interest; 2) specifies a learner of the unknown regression; and 3) uses the Riesz regression estimator of α0 determined by steps 1) and 2) .  \nA second innovation of this paper ","cbCaiiEA8rEX6mKe","https://ap.wps.com/l/cbCaiiEA8rEX6mKe","pdf",545038,1,29,"English","en",105,"# Introduction\n## Bias from regularization and model selection\n## Riesz representers and Riesz regression\n## Generalized regressions and weighted Riesz regression\n## Contributions: bounds and convergence rates","[{\"question\":\"Why can machine-learning estimators be biased in high-dimensional regression settings?\",\"answer\":\"Regularization and model selection bias the machine learner, and this bias can transfer through plug-in formulas for parameters of interest, making the resulting estimator highly biased.\"},{\"question\":\"How does debiased machine learning reduce bias?\",\"answer\":\"It employs Neyman orthogonal estimating equations so that the learner has zero first-order effect on the bias, with cross-fitting (sample splitting) further helping.\"},{\"question\":\"What is the main innovation regarding estimating Riesz representers?\",\"answer\":\"The paper provides Riesz regression estimators for Riesz representers that rely on the parameter of interest and the regression, avoiding explicit formulas and supporting end-to-end use of any machine learner.\"}]","Automatic Debiased Machine Learning via Riesz Regression | 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can machine-learning estimators be biased in high-dimensional regression settings?","Question",{"text":75,"@type":76},"Regularization and model selection bias the machine learner, and this bias can transfer through plug-in formulas for parameters of interest, making the resulting estimator highly biased.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does debiased machine learning reduce bias?",{"text":80,"@type":76},"It employs Neyman orthogonal estimating equations so that the learner has zero first-order effect on the bias, with cross-fitting (sample splitting) further helping.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the main innovation regarding estimating Riesz representers?",{"text":84,"@type":76},"The paper provides Riesz regression estimators for Riesz representers that rely on the parameter of interest and the regression, avoiding explicit formulas and supporting end-to-end use of any machine 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