A data-driven approach to finding K for K nearest neighbor matching in average causal effect estimation
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Date
2023
Authors
Xu, T.
Zhang, Y.
Li, J.
Liu, L.
Xu, Z.
Cheng, D.
Feng, Z.
Editors
Zhang, F.
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Book chapter
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Event/exhibition information: Web Information Systems Engineering – WISE 2023, Melbourne, 25/10/2023-27/10/2023
Source details - Title: Web Information Systems Engineering –WISE 202324th International Conference Melbourne, VIC, Australia, October 25–27, 2023 Proceedings, 2023 / Zhang, F. (ed./s), vol.14306 LNCS, Ch.55, pp.723-732
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Abstract
In causal inference, a fundamental task is to estimate causal effects using observational data with confounding variables. K Nearest Neighbor Matching (K-NNM) is a commonly used method to address confounding bias. However, the traditional K-NNM method uses the same K value for all units, which may result in unacceptable performance in real-world applications. To address this issue, we propose a novel nearest-neighbor matching method called DK-NNM, which uses a data-driven approach to searching for the optimal K values for different units. DK-NNM first reconstructs a sparse coefficient matrix of all units via sparse representation learning for finding the optimal K value for each unit.
Then, the joint propensity scores and prognostic scores are utilized to deal with high-dimensional covariates when performing K nearest-neighbor matching with the obtained K value for a unit. Extensive experiments are conducted on both semi-synthetic and real-world datasets, and the results demonstrate that the proposed DK-NNM method outperforms the state-of-the-art causal effect estimation methods in estimating average causal effects from observational data.
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Copyright 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Access Condition Notes: Accepted manuscript available after 1 October 2025