ClusterSC: Advancing Synthetic Control with Donor Selection

Saeyoung Rho, Andrew Tang, Noah Bergam, Rachel Cummings, Vishal Misra
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, PMLR 258:109-117, 2025.

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 this Paper

BibTeX
@InProceedings{pmlr-v258-rho25a, 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}, pages = {109--117}, year = {2025}, editor = {Li, Yingzhen and Mandt, Stephan and Agrawal, Shipra and Khan, Emtiyaz}, volume = {258}, series = {Proceedings of Machine Learning Research}, month = {03--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v258/main/assets/rho25a/rho25a.pdf}, url = {https://proceedings.mlr.press/v258/rho25a.html}, 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.} }
Endnote
%0 Conference Paper %T ClusterSC: Advancing Synthetic Control with Donor Selection %A Saeyoung Rho %A Andrew Tang %A Noah Bergam %A Rachel Cummings %A Vishal Misra %B Proceedings of The 28th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2025 %E Yingzhen Li %E Stephan Mandt %E Shipra Agrawal %E Emtiyaz Khan %F pmlr-v258-rho25a %I PMLR %P 109--117 %U https://proceedings.mlr.press/v258/rho25a.html %V 258 %X 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.
APA
Rho, S., Tang, A., Bergam, N., Cummings, R. & Misra, V.. (2025). ClusterSC: Advancing Synthetic Control with Donor Selection. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 258:109-117 Available from https://proceedings.mlr.press/v258/rho25a.html.

Related Material