  Emissions generators, such as coal-fired power plants, are key contributors
to air pollution and thus environmental policies to reduce their emissions have
been proposed. Furthermore, marginalized groups are exposed to
disproportionately high levels of this pollution and have heightened
susceptibility to its adverse health impacts. As a result, robust evaluations
of the heterogeneous impacts of air pollution regulations are key to justifying
and designing maximally protective interventions. However, such evaluations are
complicated in that much of air pollution regulatory policy intervenes on large
emissions generators while resulting impacts are measured in potentially
distant populations. Such a scenario can be described as that of bipartite
network interference (BNI). To our knowledge, no literature to date has
considered estimation of heterogeneous causal effects with BNI. In this paper,
we contribute to the literature in a three-fold manner. First, we propose
BNI-specific estimators for subgroup-specific causal effects and design an
empirical Monte Carlo simulation approach for BNI to evaluate their
performance. Second, we demonstrate how these estimators can be combined with
subgroup discovery approaches to identify subgroups benefiting most from air
pollution policies without a priori specification. Finally, we apply the
proposed methods to estimate the effects of coal-fired power plant emissions
control interventions on ischemic heart disease (IHD) among 27,312,190 US
Medicare beneficiaries. Though we find no statistically significant effect of
the interventions in the full population, we do find significant IHD
hospitalization decreases in communities with high poverty and smoking rates.
