Activity Number:
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325
- Bayesian Methods for Policy Research
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Type:
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Invited
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Date/Time:
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Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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Health Policy Statistics Section
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Abstract #326696
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Presentation
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Title:
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Evaluating Power Plant Regulations with Methods for Causal Inference on Bipartite Networks with Interference
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Author(s):
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Fabrizia Mealli* and Corwin Zigler and Laura Forastiere
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Companies:
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University of Florence and Harvard T.H. Chan School of Public Health and University of Florence
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Keywords:
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interference;
networks;
environmental regulations
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Abstract:
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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.
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Authors who are presenting talks have a * after their name.