Panel data consists of a collection of $N$ units that are observed over $T$ units of time. A policy or treatment is subject to staggered adoption if different units take on treatment at different times and remains treated (or never at all). Assessing the effectiveness of such a policy requires estimating the treatment effect, corresponding to the difference between outcomes for treated versus untreated units. We develop inference procedures that build upon a computationally efficient matrix estimator for treatment effects in panel data. Our routines return confidence intervals (CIs) both for individual treatment effects, as well as for more general bilinear functionals of treatment effects, with prescribed coverage guarantees. We apply these inferential methods to analyze the effectiveness of Medicaid expansion portion of the Affordable Care Act. Based on our analysis, Medicaid expansion has led to substantial reductions in uninsurance rates, has reduced infant mortality rates, and has had no significant effects on healthcare expenditures.

翻译：面板数据包含 $N$ 个单元在 $T$ 个时间单位上的观测集合。若不同单元在不同时间点接受处理并保持处理状态（或始终未接受处理），则该政策或处理呈现交错采纳特征。评估此类政策的有效性需估计处理效应，即处理单元与未处理单元结果之间的差异。我们基于面板数据中处理效应的计算高效矩阵估计量，开发了相应的推断程序。我们的方法可为个体处理效应，以及更一般的处理效应双线性泛函，提供具有预设覆盖保证的置信区间。我们将这些推断方法应用于分析《平价医疗法案》中医疗补助扩展政策的有效性。基于分析，医疗补助扩展显著降低了未参保率，减少了婴儿死亡率，且对医疗支出未产生显著影响。