Although some pollutants emitted in vehicle exhaust, such as benzene, are known to cause leukemia in adults with high exposure levels, less is known about the relationship between traffic-related air pollution (TRAP) and childhood hematologic cancer. In the 1990s, the US EPA enacted the reformulated gasoline program in select areas of the US, which drastically reduced ambient TRAP in affected areas. This created an ideal quasi-experiment to study the effects of TRAP on childhood hematologic cancers. However, existing methods for quasi-experimental analyses can perform poorly when outcomes are rare and unstable, as with childhood cancer incidence. We develop Bayesian spatio-temporal matrix completion methods to conduct causal inference in quasi-experimental settings with rare outcomes. Selective information sharing across space and time enables stable estimation, and the Bayesian approach facilitates uncertainty quantification. We evaluate the methods through simulations and apply them to estimate the causal effects of TRAP on childhood leukemia and lymphoma.

翻译：尽管已知汽车尾气中排放的某些污染物（如苯）在高暴露水平下会导致成人白血病，但交通相关空气污染（TRAP）与儿童血液系统癌症之间的关系尚不明确。20世纪90年代，美国环境保护署在美国部分地区实施了汽油配方改革计划，显著降低了受影响区域的TRAP水平。这为研究TRAP对儿童血液系统癌症的影响创造了理想的准实验条件。然而，当结局变量罕见且不稳定（如儿童癌症发病率）时，现有准实验分析方法的表现可能不佳。我们开发了贝叶斯时空矩阵补全方法，可在罕见结局的准实验环境中进行因果推断。通过跨空间与时间的选择性信息共享实现稳定估计，贝叶斯方法则有助于量化不确定性。我们通过模拟实验评估了该方法，并将其应用于估计TRAP对儿童白血病和淋巴瘤的因果效应。