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Therefore, several machine learning techniques have been proposed, most of them focusing on leveraging the predictive power of neural network models to attain more precise estimation of causal effects. In this work, we propose a new methodology, named Nearest Neighboring Information for Causal Inference (NNCI), for integrating valuable nearest neighboring information on neural network-based models for estimating treatment effects. The proposed NNCI methodology is applied to some of the most well established neural network-based models for treatment effect estimation with the use of observational data. Numerical experiments and analysis provide empirical and statistical evidence that the integration of NNCI with state-of-the-art neural network models leads to considerably improved treatment effect estimations on a variety of well-known challenging benchmarks. <\/jats:p>","DOI":"10.1142\/s0129065723500363","type":"journal-article","created":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T08:54:22Z","timestamp":1681980862000},"source":"Crossref","is-referenced-by-count":9,"title":["Integrating Nearest Neighbors with Neural Network Models for Treatment Effect Estimation"],"prefix":"10.1142","volume":"33","author":[{"given":"Niki","family":"Kiriakidou","sequence":"first","affiliation":[{"name":"Department of Informatics and Telematics, Harokopio University of Athens, Omirou 9, Athens 177 78, Greece"}]},{"given":"Christos","family":"Diou","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telematics, Harokopio University of Athens, Omirou 9, Athens 177 78, Greece"}]}],"member":"219","published-online":{"date-parts":[[2023,6,17]]},"reference":[{"issue":"4","key":"S0129065723500363BIB001","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1097\/EDE.0b013e3181a663cc","volume":"20","author":"Schneeweiss S.","year":"2009","journal-title":"Epidemiology (Cambridge, Mass.)"},{"issue":"2","key":"S0129065723500363BIB002","doi-asserted-by":"crossref","first-page":"2250002","DOI":"10.1142\/S0129065722500022","volume":"32","author":"Zheng S.","year":"2022","journal-title":"Int. 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