# Large Sample Properties of Matching for Balance

arXiv: https://arxiv.org/abs/1905.11386 · HTML: https://mlsanity.com/paper/1905.11386/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2019.

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

- [Optimal transport weights for causal inference](https://mlsanity.com/paper/2109.01991/) (2021)

## Abstract (arXiv)

Matching methods are widely used for causal inference in observational studies. Among them, nearest neighbor matching is arguably the most popular. However, nearest neighbor matching does not generally yield an average treatment effect estimator that is √n-consistent (Abadie and Imbens, 2006). Are matching methods not √n-consistent in general? In this paper, we study a recent class of matching methods that use integer programming to directly target aggregate covariate balance as opposed to finding close neighbor matches. We show that under suitable conditions these methods can yield simple estimators that are √n-consistent and asymptotically optimal.