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On L2-Consistency of Nearest Neighbor Matching | IEEE Journals & Magazine | IEEE Xplore

On L2-Consistency of Nearest Neighbor Matching


Abstract:

Biased sampling and missing data complicates statistical problems ranging from causal inference to reinforcement learning. We often correct for biased sampling of summary...Show More

Abstract:

Biased sampling and missing data complicates statistical problems ranging from causal inference to reinforcement learning. We often correct for biased sampling of summary statistics with matching methods and importance weighting. In this paper, we study nearest neighbor matching (NNM), which makes estimates of population quantities from biased samples by substituting unobserved variables with their nearest neighbors in the biased sample. We show that NNM is L^{2} -consistent in the absence of smoothness and boundedness assumptions in finite dimensions. We discuss applications of NNM, outline the barriers to generalizing this work to separable metric spaces, and compare NNM to inverse probability weighting.
Published in: IEEE Transactions on Information Theory ( Volume: 69, Issue: 6, June 2023)
Page(s): 3978 - 3988
Date of Publication: 02 December 2022

ISSN Information:


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