Researchers are often faced with evaluating the effect of a policy or program that was simultaneously initiated across an entire population of units at a single point in time, and its effects over the targeted population can manifest at any time period afterwards. In the presence of data measured over time, Bayesian time series models have been used to impute what would have happened after the policy was initiated, had the policy not taken place, in order to estimate causal effects. However, the considerations regarding the definition of the target estimands, the underlying assumptions, the plausibility of such assumptions, and the choice of an appropriate model have not been thoroughly investigated. In this paper, we establish useful estimands for the evaluation of large-scale policies. We discuss that imputation of missing potential outcomes relies on an assumption which, even though untestable, can be partially evaluated using observed data. We illustrate an approach to evaluate this key causal assumption and facilitate model elicitation based on data from the time interval before policy initiation and using classic statistical techniques. As an illustration, we study the Hospital Readmissions Reduction Program (HRRP), a US federal intervention aiming to improve health outcomes for patients with pneumonia, acute myocardial infraction, or congestive failure admitted to a hospital. We evaluate the effect of the HRRP on population mortality across the US and in four geographic subregions, and at different time windows. We find that the HRRP increased mortality from the three targeted conditions across most scenarios considered, and is likely to have had a detrimental effect on public health.
翻译:研究人员往往面临评估在某一时间点同时针对单位全体人口启动的政策或方案的影响,而政策或方案对目标人口的影响在其后任何时间段都可能显现出来。在有时间段测得的数据的情况下,巴伊西亚时间序列模型被用于估算政策启动后可能发生的情况,如果政策没有实施,以估计因果关系。然而,关于目标估计的定义、基本假设、此类假设的可信任性以及适当模式的选择的考虑尚未得到彻底调查。在本文件中,我们为评估大规模政策制定了有用的估算值。我们讨论的是,对缺失的潜在结果的估算值取决于一种假设,这种假设虽然无法检验,但可以部分地利用观察到的数据来评估政策启动后发生的情况。我们展示了评估这一关键因果关系假设的方法,并便利根据政策启动之前的时间段的数据以及使用典型的统计技术对模型进行推断。我们研究了医院阅读减少方案(HRRP ),美国联邦的一项干预旨在改进大规模政策评估的危害性估算值。 我们从三种公共卫生和健康后果的预测性结果中,我们从三种病变的死亡率评估了三种病变结果,我们从三种病变的病变的死亡率到急性的死亡率。