AI 中文总结
该研究针对小样本短面板的政策评估场景，通过校准模拟评估SDiD和ASCM的性能，为其可靠应用提供实用指导。
AI 中文摘要
纵向准实验场景下的因果效应估计方法广泛应用于经济学、公共卫生、政治学等领域。然而，卫生政策效应研究常依赖有限的样本量，包括研究单位和分析的时间周期。合成双重差分法（SDiD）与增强合成控制法（ASCM）是近期开发的政策干预效应评估方法。尽管SDiD和ASCM通常比双重差分法（DiD）及合成控制法（SCM）依赖更弱的假设，但在小样本量或短面板长度的相关场景中，它们缺乏关于偏差或覆盖率的理论性能保证。为评估SDiD和ASCM在真实小样本场景中的性能，我们采用校准模拟策略，该策略可在目标场景中向现有数据注入已知的处理效应。基于这些实证研究的发现，我们为研究人员和政策制定者提供实用指导，说明在真实场景下SDiD和ASCM何时可能产生可靠的估计与推断。
英文摘要
Methods for estimating causal effects in longitudinal, quasi-experimental settings are widely used in economics, public health, political science, and other fields. However, studies evaluating effects of health policies often rely on limited sample sizes, both in terms of study units and time periods analyzed. Synthetic difference-in-differences (SDiD) and the augmented synthetic control method (ASCM) are recently developed methods for evaluating the effects of policy interventions. Although SDiD and ASCM generally rely on weaker assumptions than both DiD and SCM, they lack theoretical performance guarantees with respect to bias or coverage in relevant settings with small sample sizes or short panel lengths. To evaluate the performance of SDiD and ASCM in realistic, small-sample settings, we employ a calibrated simulation strategy that allows the injection of a known treatment effect into existing data in a setting of interest. Drawing on findings from these empirical investigations, we offer practical guidance for researchers and policymakers on when SDiD and ASCM are likely to yield reliable estimates and inferences under realistic scenarios.
