Network-Constrained Matching and Randomization-Based Inference for Causal Effects
Abstract
Matching is widely used to mimic randomized experiments by forming matched sets in which treated and control units differ only randomly with respect to observed covariates. However, when the study population consists of interconnected units from a single network or a small number of networks, matching solely on observed covariates may produce matched units that are more closely connected in the network than would occur by chance. This increased network proximity within matched sets can induce spurious associations between treatment and outcome when both variables exhibit similar autocorrelation patterns on the network. To reduce spurious associations while preserving the validity of causal comparisons, we propose a new matching method that matches units with similar covariates subject to additional network-proximity constraints. For post-matching inference, we propose a randomization-based procedure for testing the sharp null hypothesis of no causal effect. The inference uses the asymptotic normal approximation and accommodates statistical dependence among test statistics obtained from each matched set without requiring explicit specifications of their correlation structures. We demonstrate the validity and utility of the proposed methods through simulation studies and apply them to the Framingham Heart Study. The matching method and subsequent inference procedure are implemented in the R package netmatchRI.
Related
- COSTA: Covariance-Optimized Design and Causal Inference under Network-Temporal Interference
- Randomization Inference: Theory and Applications
- Selective Randomization Inference for Interference
- Randomization Inference with Concentration Inequalities
- Randomization Inference for Matched Pairs with Binary Outcomes
BibTeX
@misc{dong2026networkconstrained,
title = {{Network-Constrained Matching and Randomization-Based Inference for Causal Effects}},
author = {Zhejia Dong and Youjin Lee},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.04102},
eprint = {2610.04102},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.04102},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Network-Constrained Matching and Randomization-Based Inference for Causal Effects
AU - Zhejia Dong
AU - Youjin Lee
PY - 2026
DA - 2026/10/02
PB - arXiv
DO - 10.48550/arxiv.2610.04102
UR - https://arxiv.org/abs/2610.04102
AB - Matching is widely used to mimic randomized experiments by forming matched sets in which treated and control units differ only randomly with respect to observed covariates. However, when the study population consists of interconnected units from a single network or a small number of networks, matching solely on observed covariates may produce matched units that are more closely connected in the network than would occur by chance. This increased network proximity within matched sets can induce spurious associations between treatment and outcome when both variables exhibit similar autocorrelation patterns on the network. To reduce spurious associations while preserving the validity of causal comparisons, we propose a new matching method that matches units with similar covariates subject to additional network-proximity constraints. For post-matching inference, we propose a randomization-based procedure for testing the sharp null hypothesis of no causal effect. The inference uses the asymptotic normal approximation and accommodates statistical dependence among test statistics obtained from each matched set without requiring explicit specifications of their correlation structures. We demonstrate the validity and utility of the proposed methods through simulation studies and apply them to the Framingham Heart Study. The matching method and subsequent inference procedure are implemented in the R package netmatchRI.
ER -