# Matching Algorithms for Causal Inference with Multiple Treatments

arXiv: https://arxiv.org/abs/1809.00269 · HTML: https://mlsanity.com/paper/1809.00269/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2018.

## Built on by (most cited first, 3 of 3)

- [Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies](https://mlsanity.com/paper/2112.10274/) (2021)
- [Active and Passive Causal Inference Learning](https://mlsanity.com/paper/2308.09248/) (2023)
- [LILI clustering algorithm: Limit Inferior Leaf Interval Integrated into Causal Forest for Causal Interference](https://mlsanity.com/paper/2507.03271/) (2025)

## Abstract (arXiv)

Randomized clinical trials (RCTs) are ideal for estimating causal effects, because the distributions of background covariates are similar in expectation across treatment groups. When estimating causal effects using observational data, matching is a commonly used method to replicate the covariate balance achieved in a RCT. Matching algorithms have a rich history dating back to the mid-1900s, but have been used mostly to estimate causal effects between two treatment groups. When there are more than two treatments, estimating causal effects requires additional assumptions and techniques. We propose matching algorithms that address the drawbacks of the current methods, and we use simulations to compare current and new methods. All of the methods display improved covariate balance in the matched sets relative to the pre-matched cohorts. In addition, we provide advice to investigators on which matching algorithms are preferred for different covariate distributions.