Causal Inference with Interference for Evaluating Air Quality Policies

 说明(由申请人提供)：公共卫生干预措施通常针对健康的上游决定因素(例如，社会或环境因素)，以促进人口健康。尽管这种干预措施是公共卫生政策的基石，但评估其有效性的因果推断方法受到当前对个人水平治疗的临床调查的关注的限制。一个极具争议的例子是一系列旨在通过限制美国发电厂有害排放来减少与污染相关的健康负担的监管政策。与临床环境不同，比较这些监管干预措施的有效性受到了挑战，因为污染排放在整个大气中演变，导致给定地点的污染和健康结果部分决定于许多发电厂采取的干预措施。一个给定的机组对多个发电厂的监管干预的依赖导致了因果推理文献中所说的干扰。干预措施在一个观察级别(如发电厂)应用，而感兴趣的结果在另一个级别(如个人或人群)衡量，这一事实为数据提供了一种两部分结构。这些特征的结合给带有干扰的两方因果推理带来了挑战。目标1开发了新的贝叶斯方法，用于在可对观测进行集群(例如，通过地理或污染传输模式)以使得干扰存在于集群内而不存在于集群之间的环境中的两部分部分干扰。目的2发展新的具有一般干扰结构的贝叶斯方法。AIM 3将我们最新开发的方法部署到关于整个美国的发电厂、排放、环境空气质量和健康结果的前所未有的数据库中，以比较减少发电厂排放的监管政策的有效性。目标4将通过开发可重复研究的工具来支持所有其他目标。我们开发和传播的方法、数据和软件将使我们能够对复杂的公共卫生干预措施的相对有效性进行系统和严格的评估，这些干预措施显示出多个级别的观测单位之间的干扰。鼓舞人心的例子是空气质量监管政策，但 这些方法将被证明适用于对各种其他类型的复杂公共卫生干预措施的评估。新开发的方法将通过放松在实践中经常违反的关键假设来推进因果推理的fi领域。我们的应用程序 评估发电厂法规的方法将为此类政策的健康影响提供fi第一个基于统计的证据，并构成对有争议的空气质量干预措施进行评估以支持政策决策的方式的范式转变。

 DESCRIPTION (provided by applicant): Public health interventions routinely target upstream determinants of health (e.g., social or environmental factors) to advance the health of populations. Even though such interventions are corner- stones of public health policy, methods for causal inference to evaluate their effectiveness are limited by a current focus on clinical investigations of individual-level therapies. One highly contentious example is the suite of reg- ulatory policies designed to reduce pollution-related health burden by limiting harmful emissions from US power plants. Unlike in clinical settings, comparing the effectiveness of these regulatory interventions is challenged by the fact that pollution emissions evolve throughout the atmosphere, rendering pollution and health outcomes at a given location determined in part by interventions taken at many power plants. A given unit's dependence on regulatory interventions at multiple power plants gives rise to what is known in the causal inference literature as interference. The fact that interventions are applied at one level of observation (e.g., power plants) and outcomes of interest are measured at another level (e.g., individuals or populations) presents a bipartite structure to the data. The combination of these features presents the challenge of bipartite causal inference with interference. Aim 1 develops new Bayesian methods for bipartite partial interference in settings where observations can be clustered (e.g., by geography or pollution transport patterns) so that interference is present within cluster but not between clusters. Aim 2 develops new Bayesian methods with general interference structures. Aim 3 deploys our newly-developed methods to an unprecedented database on power plants, emissions, ambient air quality, and health outcomes across the entire US to compare the effectiveness of regulatory policies for reducing power plant emissions. Aim 4 will support all other aims with the development of tools for reproducible research. The methods, data, and software we develop and disseminate will allow systematic and rigorous evaluation of the comparative effectiveness of complex public health interventions that exhibit interference among multiple levels of observational unit. The motivating example is air quality regulatory policy, but the methods will prove applicable to the evaluation of a variety of other types of complex public health interventions. The newly-developed methods will advance the field of causal inference through relaxation of key assumptions that are routinely violated in prac- tice. Application of our methods to the evaluation of power plant regulations will provide the first statistically-based evidence of the health impacts of such policies and constitute a paradigm shift in the way controversial air quality interventions are evaluated to support policy decisions.