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Friday, January 12
Fri, Jan 12, 8:30 AM - 10:15 AM
Crystal Ballroom A
Statistical Methods for Environmental Health Policy

Evaluating power plant regulations with methods for causal inference on bipartite networks with interference (303968)

Laura Forastiere, University of Florence 
*Fabrizia Mealli, University of Florence 
Corwin M Zigler, Harvard TH Chan School of Public Health 

Keywords: interference, networks, environmental regulations

Statistical methods to evaluate the effectiveness of interventions are increasingly challenged by the inherent interconnectedness of units. Specifically, a recent flurry of methods research has addressed the problem of interference between observations, which arises when one observational unit’s outcome depends not only on its treatment but also the treatment assigned to other units. We consider the setting of bipartite causal inference with interference, which arises when 1) treatments are defined on observational units that are distinct from those at which outcomes are measured and 2) there is interference between units in the sense that outcomes for some units depend on the treatments assigned to many other units. We define relevant estimands and propose a novel generalized propensity score approach to estimation in the context of evaluating air quality interventions on power plants in the United States.