High-Dimensional Covariate Matching via Sufficient Dimension Reduction

High-Dimensional Covariate Matching via Sufficient Dimension Reduction | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 1 October 2025 V1 Latest version Share on High-Dimensional Covariate Matching via Sufficient Dimension Reduction Authors : Debo Cheng , Yang Xie , Jiuyong Li , Lin Liu , Tingting Xu , Yinghao Zhang 0000-0001-6084-3278 [email protected] , and Zaiwen Feng Authors Info & Affiliations https://doi.org/10.22541/au.175929849.97905588/v1 146 views 170 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Estimating the causal effect of a treatment on an outcome of interest, i.e., causal effect estimation, is critical to understanding the mechanism underlying the data in various domains. However, the estimation of causal effects, particularly with high-dimensional data, is challenging due to the presence of high-dimensional catastrophes and complex relationships between variables. While matching is a commonly used method for causal effect estimation from observational data, it fails to address the curse of dimensionality associated with high-dimensional data. In this work, we propose a solution to the dimensionality catastrophe problem using the property of sufficient dimension reduction (SDR) method for the average treatment effect (ATE) and conditional average treatment effect (CATE) estimation. Theoretically, we demonstrate that the reduced covariates corresponding to the outcome variable obtained through SDR is a prognostic score for ATE and CATE estimation. Based on the proposed theorem, we propose an efficient nearest neighbor M _ atching method (MIRE) using the I _ nverse R _ egression E _ stimator to learn a low-dimensional set from the original covariates for ATE and CATE estimation. Experimental results on two semi-synthetic datasets show that the proposed MIRE method is sufficient for ATE and CATE estimation from observational data. Furthermore, experiments conducted on real-world data have shown the potential of the proposed MIRE method. Supplementary Material File (main.pdf) Download 920.52 KB Information & Authors Information Version history V1 Version 1 01 October 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords causal inference confounding bias nearest neighbor matching sufficient dimension reduction Authors Affiliations Debo Cheng Hainan University School of Computer Science and Technology View all articles by this author Yang Xie Huazhong Agriculture University College of informatics View all articles by this author Jiuyong Li University of South Australia STEM Academic Unit View all articles by this author Lin Liu University of South Australia STEM Academic Unit View all articles by this author Tingting Xu Huazhong Agriculture University College of informatics View all articles by this author Yinghao Zhang 0000-0001-6084-3278 [email protected] Huazhong Agriculture University College of informatics View all articles by this author Zaiwen Feng Huazhong Agriculture University College of informatics View all articles by this author Metrics & Citations Metrics Article Usage 146 views 170 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Debo Cheng, Yang Xie, Jiuyong Li, et al. High-Dimensional Covariate Matching via Sufficient Dimension Reduction. Authorea . 01 October 2025. DOI: https://doi.org/10.22541/au.175929849.97905588/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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