平均处理效应局部化：合成控制中的投影方法
Average Treatment Effect Localization: Projection Methods in Synthetic Control
- Rutgers University（罗格斯大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出平均处理效应局部化（ATEL）方法，利用多样化投影估计时变因子模型，以在面板数据中评估短期政策影响，并应用于“持有权”法律对暴力犯罪率的影响分析。
AI中文摘要：
许多现实世界的政策和商业干预需要评估短期效应以支持及时决策，尽管大多数因果推断方法侧重于长期平均处理效应。在本文中，我们引入了平均处理效应局部化（ATEL），该方法在具有单一处理单位的面板数据设置中捕捉局部化的短期政策影响，并提供政策影响的早期指标。为了适应观测和未观测协变量的时变及非线性效应，我们提出了一种针对未处理结果的非参数模型，通过筛近似将其解释为时变因子模型。由于边界偏差和识别问题，估计时变因子模型具有挑战性。我们基于多样化投影的估计方法可以有效解决这些问题。我们发展了渐近分布理论以促进ATEL估计量的推断。在实证应用中，我们将所提出的方法应用于评估“持有权”法律对暴力犯罪率的影响。
英文摘要：
Many real-world policies and business interventions require assessing short-term effects to inform timely decisions, even though most causal inference methods focus on long-term average treatment effects. In this paper, we introduce average treatment effect localization (ATEL), which captures localized, short-term policy impacts in panel data settings with a single treated unit and provides early indicators of policy impact. To accommodate both time-varying and nonlinear effects of observed and unobserved covariates, we propose a nonparametric model for untreated outcome, interpreted as a time-varying factor model via sieve approximation. Estimating the time-varying factor model is challenging due to the boundary bias and identification. Our estimation method based on diversified projection can effectively address these issues. We develop an asymptotic distribution theory to facilitate inference for the ATEL estimator. In an empirical application, we apply our proposed methodology to assess the impact of right-to-carry laws on violent crime rate.
