[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119568-en":3,"doc-seo-119568-105":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":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},119568,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Estimating Distributional Treatment Effects in Randomized Experiments - Machine Learning for Variance Reduction","A novel regression adjustment method is developed for estimating distributional treatment effect parameters in randomized experiments. While randomized trials are widely used to estimate average effects, richer policy and scientific insights require distributional quantities. The method integrates pre-treatment covariates into a distributional regression framework and employs machine learning models to improve estimator precision. Cross-fitting mitigates sensitivity to nuisance estimation errors, and theoretical asymptotic results support uniformly valid inference, validated through simulations and real data.","Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction  \nUndral Byambadalai 1 Tatsushi Oka 2 Shota Yasui 1  \narXiv :2407 . 16037v1 [ econ .EM] 22 Jul 2024  \nAbstract  \nWe propose a novel regression adjustment method designed for estimating distributional treatment effect parameters in randomized experiments.  \nRandomized experiments have been extensively used to estimate treatment effects in various scientific fields. However, to gain deeper insights, it is essential to estimate distributional treatment effects rather than relying solely on average effects.  \nOur approach incorporates pre-treatment covariates into a distributional regression framework, utilizing machine learning techniques to improve the precision of distributional treatment effect estimators. The proposed approach can be readily implemented with off-the-shelf machine learning methods and remains valid as long as the nuisance components are reasonably well estimated.  \nAlso, we establish the asymptotic properties of the proposed estimator and present a uniformly valid inference method. Through simulation results and real data analysis, we demonstrate the effectiveness of integrating machine learning techniques in reducing the variance of distributional treatment effect estimators in finite samples.  \n1. Introduction  \nRandomized experiments have played a crucial role in understanding the effects of interventions and guiding policy decisions, ever since the seminal work by Fisher (1935) . The estimation of causal effects through randomized experiments has found widespread application across various scientific disciplines (Rubin, 1974 ; Heckman et al., 1997 ; Imai, 2005 ; Imbens & Rubin, 2015) and has also become a  \n1 CyberAgent, Inc., Tokyo, Japan 2Department of Economics, Keio University, Tokyo, Japan. Correspondence to: Undral Byambadalai \u003Cundral [byambadalai@cyberagent.co.jp](byambadalai@cyberagent.co.jp) >, Tatsushi Oka \u003C[tatsushi.oka@keio.jp](tatsushi.oka@keio.jp) >, Yasui Shota \u003Cyasui [shota@cyberagent.co.jp](shota@cyberagent.co.jp) >.  \nProceedings of the 41 st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024 . Copyright 2024 by the author(s) .  \nstandard practice within the technology sector (Tang et al., 2010 ; Bakshy et al., 2014 ; Xie & Aurisset, 2016 ; Kohaviet al., 2020) .  \nWhen analyzing data from randomized experiments, one commonly used measure is the Average Treatment Effect (ATE) . However, it is often the case that understanding the distributional treatment effects can provide a richer perspective than solely focusing on overall average effects. Furthermore, while randomized experiments simplify outcomebased analysis, pre-treatment auxiliary information is frequently available. This quest for a more comprehensive understanding of treatment effects, marked by supplementing auxiliary data, calls for new approaches to enhance precision through pre-treatment data incorporation.  \nIn this work, we propose a novel regression-adjustment method to estimate a wide range of distributional parameters in the randomized experiment setup. Our approach draws inspiration from the generic Neyman-orthogonal moment condition (Chernozhukov et al., 2018), which facilitates the decoupling of nuisance parameter and treatment effect estimation into two stages. The nuisance parameters of our interest are the conditional outcome distributions given pretreatment covariates, and we propose the use of machine learning models (e.g., LASSO, random forests, neural networks, etc.), allowing for complex data and distributional structures. By integrating these sophisticated machine learning techniques with cross-fitting, we reduce the sensitivity of our treatment effect estimator to errors arising from nuisance parameter estimation.  \nOur paper makes several noteworthy contributions. First, our approach expands the scope of regression adjustment. While regression adjustment is commonly emplo","cbCait8VOkwIvIJX","https://ap.wps.com/l/cbCait8VOkwIvIJX","pdf",804796,1,32,"English","en",105,"# Introduction\n## Related Work\n## Problem Setup and Notations\n## Regression-Adjusted Estimators for Distributional Parameters\n## Asymptotic Results\n## Empirical Results\n## Conclusion\n## Appendix","[{\"question\":\"What problem does the paper address in randomized experiments?\",\"answer\":\"It focuses on estimating distributional treatment effect parameters rather than relying only on average treatment effects, aiming for deeper insights into how interventions change outcome distributions.\"},{\"question\":\"How does the proposed method use machine learning?\",\"answer\":\"It incorporates pre-treatment covariates in a distributional regression framework and uses machine learning models (e.g., LASSO, random forests, neural networks) to estimate nuisance components more accurately.\"},{\"question\":\"How does the paper ensure the estimator is robust to nuisance estimation errors?\",\"answer\":\"It integrates cross-fitting so the treatment effect estimator is less sensitive to errors made when estimating nuisance components.\"}]","Estimating Distributional Treatment Effects in Randomized Experiments - 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