  Evaluating air quality interventions is confronted with the challenge of
interference since interventions at a particular pollution source likely impact
air quality and health at distant locations and air quality and health at any
given location are likely impacted by interventions at many sources. The
structure of interference in this context is dictated by complex atmospheric
processes governing how pollution emitted from a particular source is
transformed and transported across space, and can be cast with a bipartite
structure reflecting the two distinct types of units: 1) interventional units
on which treatments are applied or withheld to change pollution emissions; and
2) outcome units on which outcomes of primary interest are measured. We propose
new estimands for bipartite causal inference with interference that construe
two components of treatment: a "key-associated" (or "individual") treatment and
an "upwind" (or "neighborhood") treatment. Estimation is carried out using a
semi-parametric adjustment approach based on joint propensity scores. A
reduced-complexity atmospheric model is deployed to characterize the structure
of the interference network by modeling the movement of air parcels through
time and space. The new methods are deployed to evaluate the effectiveness of
installing flue-gas desulfurization scrubbers on 472 coal-burning power plants
(the interventional units) in reducing Medicare hospitalizations among
21,577,552 Medicare beneficiaries residing across 25,553 ZIP codes in the
United States (the outcome units).
