[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119046-en":3,"doc-seo-119046-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},119046,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Hyperparameter Tuning for Causal Inference with Double Machine Learning - A Simulation Study","Proper hyperparameter tuning is crucial for maximizing performance of modern machine learning methods used for causal inference. This paper empirically studies how predictive accuracy of machine learning learners relates to causal estimation quality within the Double Machine Learning (DML) framework. Using an extensive simulation based on the 2019 Atlantic Causal Inference Conference Data Challenge, it evaluates the effect of data splitting schemes, the selection of ML methods and hyperparameters (including AutoML), and whether causal-model choices can be justified using predictive performance metrics.","arXiv :2402 .04674v1 [ econ .EM] 7 Feb 2024  \nHyperparameter Tuning for Causal Inference with Double Machine  \nLearning: A Simulation Study  \nPhilipp Bach  \nUniversity of Hamburg, Germany  \nOliver Schacht  \nUniversity of Hamburg, Germany  \nVictor Chernozhukov  \nMassachusetts Institute of Technology  \nSven Klaassen  \nUniversity of Hamburg, Germany Economic AI  \nMartin Spindler  \nUniversity of Hamburg, Germany Economic AI  \nPHILIPP. BACH @UNI-HAMBURG . DE OLIVER . SCHACHT @UNI-HAMBURG . DE  \nVCHERN @MIT. EDU SVEN . KLAASSEN @UNI-HAMBURG . DE  \nMARTIN . SPINDLER @UNI-HAMBURG . DE  \nAbstract  \nProper hyperparameter tuning is essential for achieving optimal performance of modern machine learning (ML) methods in predictive tasks. While there is an extensive literature on tuning ML learners for prediction, there is only little guidance available on tuning ML learners for causal machine learning and how to select among different ML learners. In this paper, we empirically assess the relationship between the predictive performance of ML methods and the resulting causal estimation based on the Double Machine Learning (DML) approach by Chernozhukov et al. (2018) .  \nDML relies on estimating so-called nuisance parameters by treating them as supervised learning problems and using them as plug-in estimates to solve for the (causal) parameter. We conduct an extensive simulation study using data from the 2019 Atlantic Causal Inference Conference Data Challenge. We provide empirical insights on the role of hyperparameter tuning and other practical decisions for causal estimation with DML. First, we assess the importance of data splitting schemes for tuning ML learners within Double Machine Learning. Second, we investigate how the choice of ML methods and hyperparameters, including recent AutoML frameworks, impacts the estimation performance for a causal parameter of interest. Third, we assess to what extent the choice of a particular causal model, as characterized by incorporated parametric assumptions, can be based on predictive performance metrics.  \nKeywords: Causal Machine Learning, Hyperparameter Tuning, Causal Inference, Double Machine Learning  \n1. Introduction  \nDouble/Debiased machine learning (DML) is an estimation framework for causal parameters based on ML-estimated nuisance functions that has been established by Chernozhukov et al. (2018) . DML combines the strengths of machine learning for prediction with estimation and inference of causal parameters. The major idea of the double machine learning framework is to make the estimation framework robust to inherent biases of ML estimation: To address the bias-variance tradeoff, ML methods introduce some regularization. Without any further adaption of the estimation procedure this will effectively translate into a bias of the causal parameter of interest. To overcome  \n© P. Bach, O. Schacht, V. Chernozhukov, S. Klaassen & M. Spindler.  \nBACH SCHACHT CHERNOZHUKOV KLAASSEN SPINDLER  \nthese shortcomings, the double machine learning approach combines three key ingredients (Bach et al., 2022, 2021): (i) Identification of causal parameters through Neyman-orthogonal moment conditions,(ii) high-quality machine learning estimators and (iii) sample splitting. Incorporating these ingredients makes it possible to establish √N convergence and asymptotic normality of the causal estimator. In recent years, the DML framework has become popular in various disciplines, including econometrics (Knaus, 2022), reinforcement learning (Narita et al., 2020) and management science (Schacht et al., 2023) . Whereas there is an extensive literature and ample benchmark studies on hyperparameter tuning approaches in predictive ML tasks, for example see Bischl et al. (2021), there is a considerable gap on the interaction between the predictive performance and the quality in terms of causal estimation. Several studies investigate model selection strategies for different estimation approaches that are based on ML, mostl","cbCaioF7pACLtrVJ","https://ap.wps.com/l/cbCaioF7pACLtrVJ","pdf",4989177,1,53,"English","en",105,"# Introduction\n## Problem motivation\n# Problem Setting: Learners, Hyperparameters and Sample Splitting\n## The role of learners in double machine learning","[{\"question\":\"What problem does the paper address about hyperparameter tuning in causal DML?\",\"answer\":\"It examines how tuning hyperparameters and selecting among ML learners affects the quality of causal estimation in the Double Machine Learning framework, not just predictive performance.\"},{\"question\":\"How does DML use machine learning to estimate causal parameters?\",\"answer\":\"DML estimates nuisance parameters via ML as supervised learning tasks, plugs them into a Neyman-orthogonal score function, and solves for the causal parameter using sample splitting for valid inference.\"},{\"question\":\"Which practical decisions are evaluated in the simulation study?\",\"answer\":\"The study investigates data splitting schemes for tuning, the choice of ML methods and hyperparameters (including AutoML), and how predictive performance metrics relate to selecting the causal model with parametric assumptions.\"}]","Hyperparameter Tuning for Causal Inference with Double Machine Learning - A Simulation Study | PDF",1785722074,134,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"hyperparameter-tuning-for-causal-inference-with-double-machine-learning-a-simulation-study","",{"@graph":36,"@context":86},[37,54,69],{"@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/hyperparameter-tuning-for-causal-inference-with-double-machine-learning-a-simulation-study/119046/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the paper address about hyperparameter tuning in causal DML?","Question",{"text":76,"@type":77},"It examines how tuning hyperparameters and selecting among ML learners affects the quality of causal estimation in the Double Machine Learning framework, not just predictive performance.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does DML use machine learning to estimate causal parameters?",{"text":81,"@type":77},"DML estimates nuisance parameters via ML as supervised learning tasks, plugs them into a Neyman-orthogonal score function, and solves for the causal parameter using sample splitting for valid inference.",{"name":83,"@type":74,"acceptedAnswer":84},"Which practical decisions are evaluated in the simulation study?",{"text":85,"@type":77},"The study investigates data splitting schemes for tuning, the choice of ML methods and hyperparameters (including AutoML), and how predictive performance metrics relate to selecting the causal model with parametric assumptions.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]