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

项目摘要 荒地火灾烟雾是美国空气污染的主要来源，与广泛的 一系列健康风险。随着气候变化，荒地火灾的数量和强度预计将增加; 因此，迫切需要准确地量化荒地火灾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。在野外大火中暴露显然可以- 不是随机的，所以我们依赖于空间相关的观察数据和因果推理。虽然有 虽然关于独立数据因果推断的文献令人印象深刻，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，空间环境方面的进展缓慢， 也就是说，在一个地点治疗的效果取决于附近地点的反应。我们也分析数据 来自Smoke Sense，一个环境保护局（EPA）赞助的公民科学项目，旨在 让市民使用智能手机应用程序（app）体验火灾烟雾的影响。公民科学 研究具有变革潜力，可以积累有价值的数据，并使公众参与科学研究，但可以 受自我选择治疗和复杂缺失数据模式的困扰。的首要主题 建议是开发一套休闲分析工具，以分析观测空间数据和数据阿里斯- 从智能手机应用程序，处理干扰，空间变化的治疗效果，信息 缺失和空间不可测量混杂因素。在目标1中，我们提供了一种新的空间干扰公式， 使用核距离函数。我们推广了边际结构模型和结构套均值模型 的设置与空间干扰，并提出双重鲁棒估计的直接和间接/溢出效应。 我们将采用这种新方法来估计荒地火灾烟雾对空气污染水平和健康负担的影响。 由于受试者对治疗的反应具有异质性，因此需要开发个性化的治疗方案。 评估策略，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， 提出了一种新的因果模型，描述了治疗效果如何随空间和不断发展的主题而变化 特色使用Smoke Sense数据，我们将估计应用程序参与度的异质效应， 采取预防措施，以减轻野火烟雾的影响。我们还提出了一个工具变量 处理信息缺失的方法，这在使用智能手机应用程序的研究中经常出现 并且如果不适当地解决，则可能导致无效的推断。在目标3中，我们在以前工作的基础上进行调整， 通过对治疗与缺失混杂因素之间的关系进行建模， 谱域和建立允许估计治疗效果的它们的相干性的条件。 这些方法将使用免费提供的软件传播，并在一系列应用中加以审查。 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.