交错采纳下的推断：以《平价医疗法案》为例的案例研究
Inference under Staggered Adoption: Case Study of the Affordable Care Act
AI总结：
本文开发了基于矩阵估计器的推断程序，用于估计面板数据交错采纳下的处理效应，并分析《平价医疗法案》中 Medicaid 扩展的效果。
摘要：
面板数据由在 $T$ 个时间单位上观测到的 $N$ 个单位的集合组成。如果不同单位在不同时间接受处理并保持被处理状态（或从未接受处理），则政策或处理属于交错采纳。评估此类政策的有效性需要估计处理效应，即已处理与未处理单位结果之间的差异。我们开发了推断程序，该程序建立在面板数据中处理效应的计算高效的矩阵估计器之上。我们的程序返回置信区间（CIs），既适用于单个处理效应，也适用于处理效应的更一般双线性泛函，并具有规定的覆盖保证。我们应用这些推断方法来分析《平价医疗法案》中 Medicaid 扩展部分的有效性。根据我们的分析，Medicaid 扩展导致未参保率大幅降低，降低了婴儿死亡率，并且对医疗保健支出没有显著影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
