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Machine learning can address predictive tasks, yet survey statisticians often need causal conclusions and portability across studies. Standard ML approaches typically yield biased causal estimates. This article introduces a double machine learning method for survey statistics, providing approximately unbiased estimators under an unconfoundedness identification strategy and enabling valid inference through relevant confounder selection in high-dimensional nonresponse panel settings.","[www.ssoar. info](www.ssoar. info)  \nUsing Double Machine Learning to Understand Nonresponse in the Recruitment of a Mixed-Mode Online Panel  \nFelderer, Barbara; Kueck , Jannis; Spindler, Martin  \nVeröffentlichungsversion / Published Version Zeitschriftenartikel / journal article  \nZur Verfügung gestellt in Kooperation mit / provided in cooperation with:  \nGESIS-Leibniz-Institut für Sozialwissenschaften  \nEmpfohlene Zitierung / Suggested Citation:  \nFelderer, B. , Kueck , J. , & Spindler, M. (2022) . Using Double Machine Learning to Understand Nonresponse in the Recruitment of a Mixed-Mode Online Panel. Social Science Computer Review, OnlineFirst, 1-21. [https://](https://)[ ](https://)[doi.org/10.1177/08944393221095194](doi.org/10.1177/08944393221095194)  \nNutzungsbedingungen:  \nDieser Text wird unter einer CC BY Lizenz (Namensnennung) zur Verfügung gestellt. Nähere Auskünfte zu den CC-Lizenzen finden Sie hier:  \n[https://creativecommons.org/licenses/by/4.0/deed.de](https://creativecommons.org/licenses/by/4.0/deed.de)  \nTerms of use:  \nThis document is made available under a CC BY Licence (Attribution). For more Information see:  \n[https://creativecommons.org/licenses/by/4.0](https://creativecommons.org/licenses/by/4.0)  \nOriginal Manuscript  \nUsing Double Machine Learning to Understand Nonresponse in the Recruitment of a  \nMixed-Mode Online Panel  \nSocial Science Computer Review 2022, Vol. 0(0) 1–21  \n© The Author(s) 2022  \nArticle reuse guidelines:  \n[sagepub.com/journals-permissions](sagepub.com/journals-permissions)  \n[DOI: 10.1177/08944393221095194](DOI: 10.1177/08944393221095194)[ ](DOI: 10.1177/08944393221095194)[journals.sagepub.com/home/ssc](journals.sagepub.com/home/ssc)  \nBarbara Felderer 1 􀀁, Jannis Kueck2 􀀁, and Martin Spindler2  \nAbstract  \nSurvey scientists increasingly face the problem of high-dimensionality in their research as digitization makes it much easier to construct high-dimensional (or “big”) data sets through tools such as online surveys and mobile applications. Machine learning methods are able to handle such data, and they have been successfully applied to solve predictive problems. However, in many situations, survey statisticians want to learn about causal relationships to draw conclusions and be able to transfer the findings of one survey to another. Standard machine learning methods provide biased estimates of such relationships. We introduce into survey statistics the double machine learning approach, which gives approximately unbiased estimators of parameters of interest, and show how it can be used to analyze survey nonresponse in a high-dimensional panel setting. The double machine learning approach here assumes unconfoundedness of variables as its identification strategy. In high-dimensional settings, where the number of potential confounders to include in the model is too large, the double machine learning approach secures valid inference by selecting the relevant confounding variables.  \nKeywords  \nmachine learning, causal inference, survey nonresponse, panel dropout, GESIS panel  \nIntroduction  \nA key attribute of“big data” is the large volume of data that is collected or generated, often for the purpose of statistical analysis (for further attributes see, for example, Japec et al., 2015) . When a large number of observed characteristics are available for only a limited number of observations, however, the high-dimensionality of the data sets poses challenges. Moreover, big data comes in a variety of forms, including many sorts of paradata (Kreuter, 2013b) such as call records, time  \n1 GESIS Leibniz Institute for the Social Sciences in Mannheim, Mannheim, Germany 2University of Hamburg, Hamburg, Germany  \nCorresponding Author:  \nBarbara Felderer, GESIS Leibniz Institute for the Social Sciences, B6, 4-5, Mannheim 68159, Germany.  \nEmail: [Barbara.Felderer@gesis.org](Barbara.Felderer@gesis.org)  \nstamps, or device-type and questionnaire-navigation data from online surveys (Callegaro, 2013","cbCaiqO8sf0OaZsW","https://ap.wps.com/l/cbCaiqO8sf0OaZsW","pdf",1282802,1,22,"English","en",105,"# Abstract\n# Introduction\n## Big data and high-dimensional survey challenges\n## Causal inference versus predictive modeling","[{\"question\":\"What problem does the paper address in survey research?\",\"answer\":\"It addresses high-dimensionality challenges in survey analysis and the resulting difficulty of drawing causal conclusions from big or mixed-mode online panel data, especially when nonresponse occurs.\"},{\"question\":\"Why are standard machine learning methods not sufficient for causal conclusions?\",\"answer\":\"Standard ML methods can produce biased estimates of causal relationships, which limits the ability to transfer findings and make valid causal inferences between surveys.\"},{\"question\":\"How does double machine learning help in this context?\",\"answer\":\"The paper applies double machine learning to obtain approximately unbiased estimators under an unconfoundedness assumption and to secure valid inference by selecting relevant confounding variables when many potential confounders are available.\"}]","Using Double Machine Learning to Understand Nonresponse in the Recruitment of a Mixed-Mode Online Panel | 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problem does the paper address in survey research?","Question",{"text":75,"@type":76},"It addresses high-dimensionality challenges in survey analysis and the resulting difficulty of drawing causal conclusions from big or mixed-mode online panel data, especially when nonresponse occurs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why are standard machine learning methods not sufficient for causal conclusions?",{"text":80,"@type":76},"Standard ML methods can produce biased estimates of causal relationships, which limits the ability to transfer findings and make valid causal inferences between surveys.",{"name":82,"@type":73,"acceptedAnswer":83},"How does double machine learning help in this context?",{"text":84,"@type":76},"The paper applies double machine learning to obtain approximately unbiased estimators under an unconfoundedness assumption and to secure valid inference by selecting relevant confounding variables when many potential confounders are 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