# On L2-consistency of nearest neighbor matching

arXiv: https://arxiv.org/abs/1902.02408 · HTML: https://mlsanity.com/paper/1902.02408/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2019.

## Built on by (most cited first, 1 of 1)

- [Multivariate root-n-consistent smoothing parameter free matching estimators and estimators of inverse density weighted expectations](https://mlsanity.com/paper/2407.08494/) (2024)

## Abstract (arXiv)

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 L2-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 this result to inverse probability weighting.