Time-Aware Synthetic Control
Abstract
The synthetic control (SC) framework is widely used for observational causal inference with time-series panel data. SC has been successful in diverse applications, but existing methods typically treat the ordering of pre-intervention time indices interchangeable. This invariance means they may not fully take advantage of temporal structure when strong trends are present. We propose Time-Aware Synthetic Control (TASC), which employs a state-space model with a constant trend while preserving a low-rank structure of the signal. TASC uses the Kalman filter and Rauch-Tung-Striebel smoother: it first fits a generative time-series model with expectation-maximization and then performs counterfactual inference. We evaluate TASC on both simulated and real-world datasets, including policy evaluation and sports prediction. Our results suggest that TASC offers advantages in settings with strong temporal trends and high levels of observation noise.
Cite
@article{arxiv.2601.03099,
title = {Time-Aware Synthetic Control},
author = {Saeyoung Rho and Cyrus Illick and Samhitha Narasipura and Alberto Abadie and Daniel Hsu and Vishal Misra},
journal= {arXiv preprint arXiv:2601.03099},
year = {2026}
}