交错处理采用下带双重差分调整的风险集转移合成控制
Risk-Set Transported Synthetic Control with Difference-in-Differences Adjustment under Staggered Treatment Adoption
- University of Calgary（卡尔加里大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对交错处理下供体集收缩问题，提出RT-SC-DiD方法，通过向转移参考收缩权重并加双重差分校正，降低反事实估计误差，实验验证其有效性。
AI中文摘要：
在交错处理采用设计中，后期处理的单元仅在自身处理开始前可作为早期处理队列的有效对照，因此可接受的供体集随事件时间而收缩。将供体池固定在最长时间范围会丢弃暂时合格的供体，而在每个时间范围独立重新估计合成控制权重则可能因供体构成变化而导致反事实不稳定。我们提出带双重差分调整的风险集转移合成控制（RT-SC-DiD）。对于每个队列和事件时间范围，该估计器在当前未处理供体上拟合权重，同时将其向一个转移参考收缩，该参考将退出供体的权重重新分配给相似的存活供体。双重差分基线校正消除持久水平差异。我们刻画了逐范围重新优化带来的失真，推导了朴素删除和重新归一化引起的载荷变化，并在对转移映射明确假设的正则性条件下给出了误差传播的条件递归界。我们还引入了供体支持诊断和仅供体安慰剂程序以选择转移惩罚。在80次重复的试点比较和单独的每值40次重复的敏感性分析中，中间转移正则化相对于独立的逐范围估计和强锚定降低了平均RMSE。这一证据支持该方法的偏差-方差动机，但并非已证明的保证。RT-SC-DiD适用于后期处理单元提供有用的短范围信息且供体支持随时间实质性收缩的场景。据我们所知，现有的交错合成控制和合成双重差分方法并未在风险集收缩时明确将队列内权重序列向转移参考进行正则化。
英文摘要：
In staggered treatment-adoption designs, later-treated units are valid controls for an earlier-treated cohort only until their own treatment begins, so the admissible donor set contracts with event time. Fixing the donor pool at the longest horizon discards temporarily eligible donors, whereas re-estimating synthetic-control weights independently at each horizon can make the counterfactual unstable as donor composition changes. We propose Risk-Set Transported Synthetic Control with Difference-in-Differences Adjustment (RT-SC-DiD). For each cohort and event-time horizon, the estimator fits weights on the currently untreated donors while shrinking them toward a transported reference that reallocates the weight of exiting donors to similar surviving donors. A DiD baseline correction removes persistent level differences. We characterize distortion from horizon-by-horizon reoptimization, derive the loading change induced by naive deletion and renormalization, and give a conditional recursive bound for error propagation under an explicitly assumed regularity condition on the transport map. We also introduce donor-support diagnostics and a donor-only placebo procedure for selecting the transport penalty. In an 80-replication pilot comparison and a separate 40-replication-per-value sensitivity analysis, intermediate transport regularization reduces average RMSE relative to independent horizon-specific estimation and strong anchoring. This evidence supports the method's bias-variance motivation but is not a proved guarantee. RT-SC-DiD is intended for settings where later-treated units provide useful short-horizon information and donor support contracts materially over time. Existing staggered synthetic-control and synthetic difference-in-differences methods do not, to our knowledge, explicitly regularize within-cohort weight sequences toward transported references as risk sets contract.
