Environmental epidemiologists are increasingly interested in establishing causality between exposures and health outcomes. A popular model for causal inference is the Rubin Causal Model (RCM), which typically seeks to estimate the average difference in study units' potential outcomes. An important assumption under RCM is no interference; that is, the potential outcomes of one unit are not affected by the exposure status of other units. The no interference assumption is violated if we expect spillover or diffusion of exposure effects based on units' proximity to other units and several other causal estimands arise. Air pollution epidemiology typically violates this assumption when we expect upwind events to affect downwind or nearby locations. This paper adapts causal assumptions from social network research to address interference and allow estimation of both direct and spillover causal effects. We use propensity score-based methods to estimate these effects when considering the effects of the Environmental Protection Agency's 2005 nonattainment designations for particulate matter with aerodynamic diameter less than 2.5 micrograms per cubic meter (PM2.5) on lung cancer incidence using county-level data obtained from the Surveillance, Epidemiology, and End Results (SEER) Program. We compare these methods in a rigorous simulation study that considers both spatially autocorrelated variables, interference, and missing confounders. We find that pruning and matching based on the propensity score produces the highest probability coverage of the true causal effects and lower mean squared error. When applied to the research question, we found protective direct and spillover causal effects.
翻译:环境流行病学家越来越关心确定接触和健康结果之间的因果关系。 流行的因果关系推断模型是鲁宾·考塞尔模型(RCM),它通常试图估计研究单位潜在结果的平均差异。 RCM下的一个重要假设是没有干扰;即一个单位的潜在结果不受其他单位的接触状况的影响。 如果我们根据单位与其他单位的距离和若干其他因果保护性估计接触效应的溢出或扩散,则不违反任何干扰假设。 当我们预期上风事件影响下风或附近地点时,空气污染流行病学通常违反这一假设。本文调整了社会网络研究的因果假设,以应对干扰,并允许估计直接和溢出因果效应。我们在考虑环境保护局2005年关于不满足的指定时,以空气动力直径低于2.5微克/每立方米(PM2.5)的速度计算出有关肺癌发病率的颗粒物的溢出或扩散效应时,我们利用从监测、流行病学和最终结果获得的州级数据,将社会网络研究的因果假设性假设(SEER)调整了直接和外溢出因效应。 我们用这些方法对真实性研究进行了精确的概率分析,我们用这些结果进行了比较。