Spatial Causal Inference for Wildland Fire Smoke Effects on Air Pollution and Health

项目总结 WillandfiRe Smoke是美国空气污染的主要贡献者，与广泛的 一系列健康风险。随着气候的变化，荒地fiRes的数量和强度预计将增加； 因此，迫切需要准确地量化荒地fiRe烟雾对空气的贡献程度。 污染水平和相应的健康负担，并评估预防措施的有效性 减轻健康负担。然而，这项工作提出了许多挑战。暴露于WillandfiRes显然可以- 不是随机的，所以我们依赖空间相关的观测数据和因果推断。当有一个 尽管在独立数据的因果推断方面有令人印象深刻的文献，但可用于空间数据的方法有限。 由于空间相关性和干扰引起的复杂性，空间环境的进展缓慢， 也就是说，一个地点的治疗效果取决于附近地点的反应。我们还分析数据 来自Smoke Sense，由环境保护局(EPA)赞助的公民科学项目，旨在 让体验fiRe烟雾影响的市民使用智能手机应用程序(APP)。公民科学 研究具有变革性的潜力，可以收集有价值的数据并让公众参与到科学fic研究中来，但可以 被自我选择的治疗和复杂的缺失数据模式所困扰。《纽约时报》的主题 建议开发一套临时分析工具来分析观测空间数据和数据ARIS- 来自智能手机应用程序，处理干扰，空间变化的处理效果，信息量 遗漏和空间无法测量的混杂因素。在目标1中，我们提出了一种新的空间干扰公式-- 使用核距离函数。推广了边际结构模型和结构嵌套均值模型 对于具有空间干扰的环境，提出了直接和间接/溢出效应的双稳健估计器。 我们将应用这一新方法来评估荒地fiRe烟雾对空气污染水平和健康负担的影响。 由于对治疗的反应对象不同，因此需要开发个性化推荐-- 治疗策略，以确定哪种治疗方法最有效，对谁有效，在什么情况下有效。在《目标2》中，我们 提出一个新的因果模型，该模型描述了治疗效果如何随空间和对象的变化而变化 特点。使用烟雾感知数据，我们将评估应用程序参与度和 预防措施，以减轻野地fiRe烟雾的影响。我们还提出了一个工具变量 处理信息缺失的方法，这在智能手机应用程序的研究中经常出现 如果处理不当，可能会导致无效推理。在目标3中，我们在以前工作的基础上进行调整 通过建立治疗与遗漏混杂因素之间关系的模型来研究遗漏空间混杂因素 光谱域和建立它们的一致性的条件，以允许估计治疗效果。 这些方法将使用免费提供的软件进行传播，并在一系列应用程序中进行审查。 因此，该项目的结果将对未来的环境健康研究产生广泛的影响。

PROJECT SUMMARY Wildland fire smoke is a major contributor to air pollution in the United States (US) and is associated with a wide range of health risks. The number and intensity of wildland fires are expected to increase with a changing climate; therefore, there is a pressing need to accurately quantify the extent to which wildland fire smoke contributes to air pollution levels and corresponding health burden, and to evaluate the effectiveness of preventative measures to mitigate the health burden. However, this work presents many challenges. Exposure to wildland fires clearly can- not be randomized, so we rely on spatially-correlated observational data and causal inference. While there is an impressive literature on causal inference for independent data, the methods available for spatial data are limited. Progress in the spatial setting has been slow due to complexities induced by spatial correlations and interference, i.e., the effect of treatment at one location depends on the response at nearby locations. We also analyze data from Smoke Sense, an Environmental Protection Agency (EPA)-sponsored citizen science project designed to engage citizens that experience the effects of fire smoke using smart-phone applications (app). Citizen science studies have transformative potential to amass valuable data and engage the public in scientific research, but can be plagued by self selection of treatment and complex missing data patterns. The overarching theme of the proposal is to develop a suite of casual analysis tools to analyze observational spatial data and data aris- ing from smart-phone applications, handling interference, spatially-varying treatment effects, informative missingness and spatial unmeasured confounders. In Aim 1, we provide a new formulation of spatial interfer- ence using kernel distance functions. We extend marginal structural models and structural nested mean models to the setting with spatial interference and propose doubly-robust estimators of direct and indirect/spillover effects. We will apply this new method to estimate wildland fire smoke effects on air pollution levels and health burden. Because of subject heterogeneity in response to treatment, it is desirable to develop personalized recommenda- tion strategies to determine which treatment works best, for whom, and under what circumstances. In Aim 2 we propose a novel causal model that describes how treatment effects vary over space and with evolving subject characteristics. Using the Smoke Sense data, we will estimate heterogeneous effects of app engagement and preventative measures to mitigate the impact of wildland fire smoke. We also propose an instrumental variable approach to handling informative missingness, which arises frequently in studies with smart phone applications and can lead to invalid inference if not properly addressed. In Aim 3 we build on our previous work to adjust for missing spatial confounders by modeling the relationship between the treatment and the missing confounders in the spectral domain and establishing conditions on their coherence that permit estimation of the treatment effect. The methods will be disseminated using freely-available software and examined over a range of applications. Therefore, the results of this project will have a broad impact on future environmental health studies.