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Abstract：
Emission control technologies installed on power plants are a key feature of many air pollution regulations in the US. While such regulations are predicated on the presumed relationships between emissions, ambient air pollution, and human health, many of these relationships have never been empirically verified. The goal of this paper is to develop new statistical methods to quantify these relationships. We frame this problem as one of mediation analysis to evaluate the extent to which the effect of a particular control technology on ambient pollution is mediated through causal effects on power plant emissions. Since power plants emit various compounds that contribute to ambient pollution, we develop new methods for multiple intermediate variables that are measured contemporaneously, may interact with one another, and may exhibit joint mediating effects. Specifically, we propose new methods leveraging two related frameworks for causal inference in the presence of mediating variables: principal stratification and causal mediation analysis. We define principal effects based on multiple mediators, and also introduce a new decomposition of the total effect of an intervention on ambient pollution into the natural direct effect and natural indirect effects for all combinations of mediators. Both approaches are anchored to the same observed-data models, which we specify with Bayesian nonparametric techniques. We provide assumptions for estimating principal causal effects, then augment these with an additional assumption required for causal mediation analysis. The two analyses, interpreted in tandem, provide the first empirical investigation of the presumed causal pathways that motivate important air quality regulatory policies.
摘要：
安装在发电厂上的排放控制技术是美国许多空气污染法规的关键特征。虽然这样的规定是基于排放之间的假定关系，环境空气污染,和人类健康，其中许多关系从未得到经验验证。本文的目标是开发新的统计方法来量化这些关系。我们将此问题定义为调解分析之一，以评估特定控制技术对环境污染的影响通过对发电厂排放的因果影响来调解的程度。由于发电厂排放的各种化合物会造成环境污染，我们开发了同时测量的多个中间变量的新方法，可以彼此互动，并可能表现出联合中介效应。具体来说,我们提出了在存在中介变量的情况下利用两个相关框架进行因果推断的新方法:主要分层和因果中介分析.我们基于多个中介来定义主要效应，并且还将干预对环境污染的总影响的新分解引入所有介质组合的自然直接影响和自然间接影响。两种方法都固定在相同的观测数据模型上，我们用贝叶斯非参数技术指定。我们提供了估计主要因果效应的假设，然后用因果中介分析所需的额外假设来补充这些假设。这两个分析，串联解释，提供对激励重要空气质量监管政策的假定因果途径的首次实证调查。
