[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117733-en":3,"doc-seo-117733-105":30,"detail-sidebar-cat-0-en-105":95},{"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},117733,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","DoubleML - An Object-Oriented Implementation of Double Machine Learning in R","The R package DoubleML implements the double/debiased machine learning framework for estimating causal parameters using machine learning methods. The approach relies on Neyman orthogonality, high-quality nuisance estimation, and sample splitting. Nuisance components are learned with state-of-the-art methods available in the mlr3 ecosystem. DoubleML supports inference in partially linear and interactive regression models and their extensions to instrumental variable estimation, and showcases reproducible examples with simulated and real data.","View metadata, citation and similar [papers at ](papers at core.ac.uk)[core.ac.uk](papers at core.ac.uk) brought to you by CORE  \n[provided by](provided by arXiv.org)[ arXiv.org](provided by arXiv.org) e-Print Archive  \narXiv :2103 .09603v1 [ stat .ML] 17 Mar 2021  \nDoubleML-An Object-Oriented Implementation of Double  \nMachine Learning in R ∗  \nPhilipp Bach†, Victor Chernozhukov‡, Malte S. Kurz†§, Martin Spindler†§  \nMarch 18, 2021  \nAbstract  \nThe R package DoubleML implements the double/debiased machine learning framework of Chernozhukov et al. (2018) . It provides functionalities to estimate parameters in causal models based on machine learning methods. The double machine learning framework consist of three key ingredients: Neyman orthogonality, high-quality machine learning estimation and sample splitting. Estimation of nuisance components can be performed by various state-of-the-art machine learning methods that are available in the mlr3 ecosystem. DoubleML makes it possible to perform inference in a variety of causal models, including partially linear and interactive regression models and their extensions to instrumental variable estimation. The object-oriented implementation of DoubleML enables a high flexibility for the model specification and makes it easily extendable. This paper serves as an introduction to the double machine learning framework and the R package DoubleML. In reproducible code examples with simulated and real data sets, we demonstrate how DoubleML users can perform valid inference based on machine learning methods.  \nKeywords—Machine Learning, Causal Inference, Causal Machine Learning, R, mlr3, Object Orientation  \n1 Introduction  \nStructural equation models provide a quintessential framework for conducting causal inference in statistics, econometrics, machine learning (ML), and other data sciences. The package DoubleML for R (R Core Team, 2020) implements partially linear and interactive structural equation and treatment effect models with high-dimensional confounding variables as considered in Chernozhukov et al. (2018) . Estimation and tuning of the machine learning models is based on the powerful functionalities provided by the mlr3 package and the mlr3 ecosystem (Lang et al., 2019) . A key element of double machine learning (DML) models are score functions identifying the estimates for the target parameter. These functions play an essential role for valid inference with machine learning methods because they have to satisfy a property called Neyman orthogonality. With the score functions as key elements, DoubleML implements double machine learning in a very general way using object orientation based on the R6 package (Chang, 2020) . Currently, DoubleML implements the double / debiased machine learning framework as established in Chernozhukov et al. (2018) for  \n• partially linear regression models (PLR),  \n• partially linear instrumental variable regression models (PLIV),  \n• interactive regression models (IRM), and  \n• interactive instrumental variable regression models (IIVM) .  \nThe object-oriented implementation of DoubleML is very flexible. The model classes DoubleMLPLR, DoubleMLPLIV, DoubleMLIRM and DoubleIIVM implement the estimation of the nuisance functions via machine learning methods and the computation of the Neyman-orthogonal score function. All other functionalities are implemented in the abstract base class DoubleML, including estimation of causal parameters, standard errors, t-tests, confidence intervals, as well  \n∗ Corresponding author: [philipp.bach@uni-hamburg.de. The](philipp.bach@uni-hamburg.de. The) complete R code used for the simulation examples is available at [https://www.bwl.uni-hamburg.de/en/statistik/forschung/software-und-daten.html. GitHub](https://www.bwl.uni-hamburg.de/en/statistik/forschung/software-und-daten.html. GitHub) repository of R package: [https://github.com/DoubleML/doubleml-for-r](https://github.com/DoubleML/doubleml-for-r) .  \n†University of Hamburg  \n‡M","cbCaiqGiovSxooiq","https://ap.wps.com/l/cbCaiqGiovSxooiq","pdf",1385128,1,40,"English","en",105,"# Introduction\n## DoubleML framework and key ingredients\n## Supported model classes\n## Object-oriented design with R6\n## Inference and extensions","[{\"question\":\"What is DoubleML for R designed to do?\",\"answer\":\"DoubleML provides an implementation of the double/debiased machine learning framework to estimate causal model parameters using machine learning methods and to enable valid inference.\"},{\"question\":\"Which key components does the double machine learning framework use?\",\"answer\":\"The framework consists of Neyman orthogonality, high-quality machine learning estimation for nuisance components, and sample splitting.\"},{\"question\":\"What model types does DoubleML currently support?\",\"answer\":\"DoubleML supports partially linear regression (PLR), partially linear instrumental variable regression (PLIV), interactive regression (IRM), and interactive instrumental variable regression (IIVM).\"},{\"question\":\"Why is the object-oriented implementation important in DoubleML?\",\"answer\":\"The object-oriented design using R6 provides flexible model specification and makes the package easily extendable with new model classes, score functions, and resampling schemes.\"}]","DoubleML - 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