Online Program Home
My Program

Abstract Details

Activity Number: 325 - Bayesian Methods for Policy Research
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: Health Policy Statistics Section
Abstract #326696 Presentation
Title: Evaluating Power Plant Regulations with Methods for Causal Inference on Bipartite Networks with Interference
Author(s): Fabrizia Mealli* and Corwin Zigler and Laura Forastiere
Companies: University of Florence and Harvard T.H. Chan School of Public Health and University of Florence
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 approach to estimation in the context of evaluating air quality interventions on power plants in the United States.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program