Online Program Home
My Program

Abstract Details

Activity Number: 226 - Causal Inference with Spatial Environmental Data
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Royal Statistical Society
Abstract #302874 Presentation
Title: Causal Inference with Interfering Units for Cluster and Population Level Treatment Allocation Programs
Author(s): Georgia Papadogeorgou* and Fabrizia Mealli and Corwin Zigler
Companies: Duke and University of Florence and University of Texas at Austin
Keywords: air pollution; causal inference; interference

Interference arises when a unit's potential outcome depends on its treatment level and on the treatment level of others. A common assumption invoked in the presence of interference is partial interference, implying that the population can be partitioned in clusters of individuals whose potential outcomes only depend on the treatment of units within the same cluster. Previous literature has defined average potential outcomes under counterfactual scenarios where treatments are randomly allocated to units. However, within clusters there may be units that are more or less likely to receive treatment. We define new estimands that describe average potential outcomes for realistic counterfactual treatment allocation programs taking into consideration the units' covariates and dependence between units' treatment assignment. We further propose entirely new estimands for population-level interventions over the collection of clusters, which correspond in the motivating setting to regulations at the federal level. Finally, we estimate effects in a comparative effectiveness study of power plant emission reduction technologies on ambient ozone pollution.

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

Back to the full JSM 2019 program