Motivated by environmental health research on air pollution, we address the challenge of estimation and uncertainty quantification of causal exposure-response function (CERF). The CERF describes the relationship between a continuously varying exposure (or treatment) and its causal effect on a outcome. We propose a new Bayesian approach that relies on a Gaussian process (GP) model to estimate the CERF. We parametrize the covariance (kernel) function of the GP to mimic matching via a Generalized Propensity Score (GPS). The tuning parameters of the matching function are chosen to optimize covariate balance. Our approach achieves automatic uncertainty evaluation of the CERF with high computational efficiency, enables change point detection through inference on derivatives of the CERF, and yields the desired separation of design and analysis phases for causal estimation. We provide theoretical results showing the correspondence between our Bayesian GP framework and traditional approaches in causal inference for estimating causal effects of a continuous exposure. We apply the methods to 520,711 ZIP-code-level observations to estimate the causal effect of long-term exposures to PM2.5 on all-cause mortality among Medicare enrollees in the United States.
翻译:在对空气污染进行环境卫生研究的推动下,我们应对因果接触-反应功能的估计和不确定性量化挑战。中央应急循环基金描述了持续变化的接触(或处理)及其对结果的因果关系之间的关系。我们提议采用新的巴伊西亚方法,以高斯进程模型为基础来估计中央应急循环基金。我们用通用预测分数(GPS)来模拟GP的共变(核心)功能。选择匹配功能的调试参数是为了优化共变平衡。我们的方法是以高计算效率对中央应急循环基金进行自动的不确定性评估,通过推断中央应急循环基金衍生物来进行改变点检测,并实现预期的因果估计设计和分析阶段的分离。我们提供理论结果,表明我们的巴伊斯政府GP框架和因果推断传统方法之间的对应关系,以估计持续接触的因果影响。我们采用了520 711 ZIP编码级观测方法,以估计长期接触PM2.5对美国MITA注册公司所有原因的死亡率的因果关系。