  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.
