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.

翻译：合成控制（SC）框架广泛用于基于时间序列面板数据的观察性因果推断。SC在多种应用中取得了成功，但现有方法通常将干预前时间索引的顺序视为可互换的。这种不变性意味着当存在强烈趋势时，它们可能无法充分利用时间结构。我们提出时间感知合成控制（TASC），该方法采用具有恒定趋势的状态空间模型，同时保留信号的低秩结构。TASC使用卡尔曼滤波器和Rauch-Tung-Striebel平滑器：首先通过期望最大化拟合生成式时间序列模型，然后进行反事实推断。我们在模拟和真实数据集（包括政策评估和体育预测）上评估了TASC。结果表明，TASC在具有强烈时间趋势和高观测噪声的场景中展现出优势。