  Wildfires have increased in frequency and severity over the past two decades,
especially in the Western United States. Beyond physical infrastructure damage
caused by these wildfire events, researchers have increasingly identified
harmful impacts of particulate matter generated by wildfire smoke on
respiratory, cardiovascular, and cognitive health. This inference is difficult
due to the spatial and temporal uncertainty regarding how much particulate
matter is specifically attributable to wildfire smoke. One factor contributing
to this challenge is the reliance on manually drawn smoke plume annotations,
which are often noisy representations limited to the United States. This work
uses deep convolutional neural networks to segment smoke plumes from
geostationary satellite imagery. We compare the performance of predicted plume
segmentations versus the noisy annotations using causal inference methods to
estimate the amount of variation each explains in Environmental Protection
Agency (EPA) measured surface level particulate matter <2.5um in diameter
($\textrm{PM}_{2.5}$).
