  Pollution from coal-fired power plants has been linked to substantial health
and mortality burdens in the US. In recent decades, federal regulatory policies
have spurred efforts to curb emissions through various actions, such as the
installation of emissions control technologies on power plants. However,
assessing the health impacts of these measures, particularly over longer
periods of time, is complicated by several factors. First, the units that
potentially receive the intervention (power plants) are disjoint from those on
which outcomes are measured (communities), and second, pollution emitted from
power plants disperses and affects geographically far-reaching areas. This
creates a methodological challenge known as bipartite network interference
(BNI). To our knowledge, no methods have been developed for conducting
quasi-experimental studies with panel data in the BNI setting. In this study,
motivated by the need for robust estimates of the total health impacts of power
plant emissions control technologies in recent decades, we introduce a novel
causal inference framework for difference-in-differences analysis under BNI
with staggered treatment adoption. We explain the unique methodological
challenges that arise in this setting and propose a solution via a data
reconfiguration and mapping strategy. The proposed approach is advantageous
because analysis is conducted at the intervention unit level, avoiding the need
to arbitrarily define treatment status at the outcome unit level, but it
permits interpretation of results at the more policy-relevant outcome unit
level. Using this interference-aware approach, we investigate the impacts of
installation of flue gas desulfurization scrubbers on coal-fired power plants
on coronary heart disease hospitalizations among older Americans over the
period 2003-2014, finding an overall beneficial effect in mitigating such
disease outcomes.
