Time-Aware Synthetic Control

• URL: http://arxiv.org/abs/2601.03099v1
• Date: Tue, 06 Jan 2026 15:30:26 GMT
• Title: Time-Aware Synthetic Control
• Abstract summary: Time-Aware Synthetic Control employs a state-space model with a constant trend while preserving a low-rank structure of the signal.<n>We evaluate TASC on both simulated and real-world datasets, including policy evaluation and sports prediction.
• Score: 4.298879439205417
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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