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 among the elderly across the US and in four geographic subregions, and at different time windows. We find that the HRRP increased mortality from pneumonia and acute myocardial infraction across at least one geographical region and time horizon, 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对各单位全体人口同时启动的政策或方案的影响,而该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。在有长期数据的情况下,如果政策没有实施,贝叶西亚时间序列模型就被用来估算政策启动后可能发生的情况,以便估计因果关系。然而,对于目标估计的定义、基本假设、此类假设的可信任性以及适当模式的选择等考虑,还没有进行彻底调查。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,这种假设即使无法检验,也可以使用观察到的数据对政策启动后发生的情况进行部分评估。我们展示了一种方法来评估这一关键因果关系假设,并根据政策启动之前的间隔数据以及使用典型的统计技术,我们研究了医院阅读减少方案(HRRP ),这是美国联邦的一项最低限度的干预,旨在改进大规模政策评估的地域风险。 我们讨论的是,在医院和四个地区,我们对于癌症的死亡率和心脏病的死亡率,我们从一个时间段里,我们从病变的死亡率评估了一种时间段,我们对癌症的死亡率的死亡率和心脏病和心脏病的死亡率的死亡率的诊断,我们对四个地区的死亡率进行了评估。