The synthetic control method (SCM) estimates causal effects in panel data with a single-treated unit by constructing a counterfactual outcome as a weighted combination of untreated control units that matches the pre-treatment trajectory. In this paper, we introduce the targeted synthetic control (TSC) method, a new two-stage estimator that directly estimates the counterfactual outcome. Specifically, our TSC method (1) yields a targeted debiasing estimator, in the sense that the targeted updating refines the initial weights to produce more stable weights; and (2) ensures that the final counterfactual estimation is a convex combination of observed control outcomes to enable direct interpretation of the synthetic control weights. TSC is flexible and can be instantiated with arbitrary machine learning models. Methodologically, TSC starts from an initial set of synthetic-control weights via a one-dimensional targeted update through the weight-tilting submodel, which calibrates the weights to reduce bias of weights estimation arising from pre-treatment fit. Furthermore, TSC avoids key shortcomings of existing methods (e.g., the augmented SCM), which can produce unbounded counterfactual estimates. Across extensive synthetic and real-world experiments, TSC consistently improves estimation accuracy over state-of-the-art SCM baselines.

翻译：合成控制方法（SCM）通过构建一个未处理控制单元的加权组合来匹配处理前轨迹，从而估计具有单一处理单元的面板数据中的因果效应。本文提出目标合成控制（TSC）方法，这是一种新的两阶段估计器，可直接估计反事实结果。具体而言，我们的TSC方法（1）生成一种目标去偏估计器，其目标更新过程能够优化初始权重以产生更稳定的权重；（2）确保最终的反事实估计是观测控制结果的凸组合，从而实现对合成控制权重的直接解释。TSC具有灵活性，可通过任意机器学习模型进行实例化。在方法论上，TSC首先通过权重倾斜子模型进行一维目标更新，从初始合成控制权重出发，校准权重以减少由处理前拟合产生的权重估计偏差。此外，TSC避免了现有方法（如增强型SCM）可能产生无界反事实估计的关键缺陷。在大量合成与真实世界实验中，TSC相较于最先进的SCM基线方法持续提升了估计精度。