ClusterSC: Advancing Synthetic Control with Donor Selection
Abstract
In causal inference with observational studies, synthetic control (SC) has emerged as a prominent tool. SC has traditionally been applied to aggregate-level datasets, but more recent work has extended its use to individual-level data. As they contain a greater number of observed units, this shift introduces the curse of dimensionality to SC. To address this, we propose Cluster Synthetic Control (ClusterSC), based on the idea that groups of individuals may exist where behavior aligns internally but diverges between groups. ClusterSC incorporates a clustering step to select only the relevant donors for the target. We provide theoretical guarantees on the improvements induced by ClusterSC, supported by empirical demonstrations on synthetic and real-world datasets. The results indicate that ClusterSC consistently outperforms classical SC approaches.
Cite
Text
Rho et al. "ClusterSC: Advancing Synthetic Control with Donor Selection." Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, 2025.Markdown
[Rho et al. "ClusterSC: Advancing Synthetic Control with Donor Selection." Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, 2025.](https://mlanthology.org/aistats/2025/rho2025aistats-clustersc/)BibTeX
@inproceedings{rho2025aistats-clustersc,
title = {{ClusterSC: Advancing Synthetic Control with Donor Selection}},
author = {Rho, Saeyoung and Tang, Andrew and Bergam, Noah and Cummings, Rachel and Misra, Vishal},
booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics},
year = {2025},
pages = {109-117},
volume = {258},
url = {https://mlanthology.org/aistats/2025/rho2025aistats-clustersc/}
}