Time-Aware Synthetic Control

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 indicies 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 stronger temporal trends and higher levels of observation noise.