[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117329-en":3,"doc-seo-117329-105":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},117329,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Comprehensive Causal Machine Learning","Comprehensive Causal Machine Learning addresses how to uncover causal effects when multiple treatments exist and effects vary across levels of granularity. The paper proposes and compares three selection-on-observables approaches—modified causal forest (mcf), generalized random forest (grf), and double machine learning (dml)—for estimating and providing inference on causal mean effects. It contrasts their theoretical properties and supplies proven guarantees for mcf, showing when each method performs best, and highlighting mcf’s robustness and internal consistency for aggregated causal parameters.","arXiv :2405 . 10198v2 [ econ .EM] 14 Feb 2025  \nComprehensive Causal Machine Learning ∗  \nMichael Lechner† Jana Mareckova  \nFebruary 17, 2025  \nComments are very welcome.  \nAbstract  \nUncovering causal effects in multiple treatment setting at various levels of granularity provides substantial value to decision makers. Comprehensive machine learning approaches to causal effect estimation allow to use a single causal machine learning approach for estimation and inference of causal mean effects for all levels of granularity. Focusing on selection-on-observables, this paper compares three such approaches, the modified causal forest (mcf ), the generalized random forest (grf ), and double machine learning (dml) . It also compares the theoretical properties of the approaches and provides proven theoretical guarantees for the mcf. The findings indicate that dml-based methods excel for average treatment effects at the population level (ATE) and group level (GATE) with few groups, when selection into treatment is not too strong. However, for finer causal heterogeneity, explicitly outcome-centred forest-based approaches are superior. The mcf has three additional benefits: (i) It is the most robust estimator in cases when dml-based approaches underperform because of substantial selection into treatment; (ii) it is the best estimator for GATEs when the number of groups gets larger; and (iii), it is the only estimator that is internally consistent, in the sense that low-dimensional causal ATEs and GATEs are obtained as aggregates of finer-grained causal parameters.  \nKeywords: Causal machine learning, statistical learning, conditional average treatment effects, individualized treatment effects, multiple treatments, selection-on-observed-variables  \nJEL classification: C21, C87  \nCorrespondence to: Michael Lechner or Jana Mareckova, Professors of Econometrics, Swiss Institute for Empirical Economic Research (SEW), University of St. Gallen, Switzerland, michael.lechner@unisg.ch, [jana.mareckova@unisg.ch](jana.mareckova@unisg.ch), [www.sew.unisg.ch](www.sew.unisg.ch).  \n∗ Results from two research projects that were part of the National Research Programmes “Big Data” (NRP 75, [www.nrp75.ch](www.nrp75.ch), grant number 407540_ 166999) and “Digital Transformation” (NRP 77, [www.nrp77.ch](www.nrp77.ch), grant number 407740_ 187301) of the Swiss National Science Foundation (SNSF) were the basis of this paper. The theoretical parton the mcf (contained in Appendix A) is a revised version of the theoretical part of our unpublished “Modified Causal Forest” paper. We thank GPT-4 for some limited assistance in editing, and Phillip Heiler, Federica Mascolo, Fabian Muny, and Hannah Busshoff for helpful comments and suggestions. The paper was presented at research seminars at the Universities of Bern and the Collegio Carlo Alberto in Turino, at an invited session of the Annual Meeting of the German Economic Association in Berlin, at CompStat in Giessen, at American Causal Inference Conference in Seattle, at COMPIE in Amsterdam and at the Workshop on Causal Inference and Machine Learning in Groningen. We are grateful to participants for helpful remarks and interesting discussions.  \n†Michael Lechner is also affiliated with Örebro University, CEPR, London, CESIfo, Munich, IAB, Nuremberg, IZA, Bonn, and RWI, Essen.  \n1 . Introduction  \nMachine learning (ML) has paved its way into academia and industry, impacting numerous fields from health care and finance to social sciences. At the core of the ML revolution is the remarkable predictive power of the methods. However, the growing debate around ML emphasizes that prediction does not imply causation. Going beyond mere predictive associations to identify cause-and-effect relationships is at the centre of most questions concerning the effects of policies, medical treatments, marketing campaigns, business decisions, etc. (see, e.g. , Athey, 2017) . The different focus of causal modelling calls for ML approaches th","cbCaibAARCpFtHrd","https://ap.wps.com/l/cbCaibAARCpFtHrd","pdf",3598916,1,153,"English","en",105,"# Introduction\n## Causal machine learning motivation\n## Problem setup: multiple treatments and aggregated effects","[{\"question\":\"What problem does the paper focus on in causal machine learning?\",\"answer\":\"The paper focuses on estimating causal effects in a multiple treatment setting under selection-on-observables, including both aggregated average effects and their finer-grained heterogeneity.\"},{\"question\":\"Which three causal ML approaches are compared?\",\"answer\":\"The paper compares modified causal forest (mcf), generalized random forest (grf), and double machine learning (dml) for causal effect estimation and inference.\"},{\"question\":\"In what situations do the authors find dml-based methods outperform others?\",\"answer\":\"dml-based methods excel for average treatment effects (ATE) and group-level effects (GATE) at the population level when there are few groups and selection into treatment is not too strong.\"}]","Comprehensive Causal Machine Learning | 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problem does the paper focus on in causal machine learning?","Question",{"text":75,"@type":76},"The paper focuses on estimating causal effects in a multiple treatment setting under selection-on-observables, including both aggregated average effects and their finer-grained heterogeneity.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which three causal ML approaches are compared?",{"text":80,"@type":76},"The paper compares modified causal forest (mcf), generalized random forest (grf), and double machine learning (dml) for causal effect estimation and inference.",{"name":82,"@type":73,"acceptedAnswer":83},"In what situations do the authors find dml-based methods outperform others?",{"text":84,"@type":76},"dml-based methods excel for average treatment effects (ATE) and group-level effects (GATE) at the population level when there are few groups and selection into treatment is not too 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