# Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference

arXiv: https://arxiv.org/abs/2003.00964 · HTML: https://mlsanity.com/paper/2003.00964/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications](https://mlsanity.com/paper/2103.04244/) (2021)
- [Neighborhood Adaptive Estimators for Causal Inference under Network Interference](https://mlsanity.com/paper/2212.03683/) (2022)
- [A Two-Part Machine Learning Approach to Characterizing Network Interference in A/B Testing](https://mlsanity.com/paper/2308.09790/) (2023)
- [Estimating causal effects with optimization-based methods: A review and empirical comparison](https://mlsanity.com/paper/2203.00097/) (2022)
- [Multiple Domain Causal Networks](https://mlsanity.com/paper/2205.06791/) (2022)
- [HNCI: High-Dimensional Network Causal Inference](https://mlsanity.com/paper/2412.18568/) (2024)
- [Treatment Effect Estimation with Differentiated Networked Effect on Graph Data](https://mlsanity.com/paper/2605.24358/) (2026)

## Abstract (arXiv)

We propose a matching method that recovers direct treatment effects from randomized experiments where units are connected in an observed network, and units that share edges can potentially influence each others' outcomes. Traditional treatment effect estimators for randomized experiments are biased and error prone in this setting. Our method matches units almost exactly on counts of unique subgraphs within their neighborhood graphs. The matches that we construct are interpretable and high-quality. Our method can be extended easily to accommodate additional unit-level covariate information. We show empirically that our method performs better than other existing methodologies for this problem, while producing meaningful, interpretable results.