Seminar: 1:00-2:30pm EST
Student Session: 3:00-4:00 EST
Bipartite Interference and Air Pollution Transport: Estimating Health Effects of Power Plant Interventions
Evaluating air quality interventions is confronted with the challenge of interference since interventions at a particular pollution source likely impact air quality and health at distant locations and air quality and health at any given location are likely impacted by interventions at many sources. The structure of interference in this context is dictated by complex atmospheric processes governing how pollution emitted from a particular source is transformed and transported across space, and can be cast with a bipartite structure reflecting the two distinct types of units: 1) interventional units on which treatments are applied or withheld to change pollution emissions; and 2) outcome units on which outcomes of primary interest are measured. We propose new estimands for bipartite causal inference with interference that construe two components of treatment: a “key-associated” (or “individual”) treatment and an “upwind” (or “neighborhood”) treatment. Estimation is carried out using a semi-parametric adjustment approach based on joint propensity scores. A reduced-complexity atmospheric model is deployed to characterize the structure of the interference network by modeling the movement of air parcels through time and space. The new methods are deployed to evaluate the effectiveness of installing flue-gas desulfurization scrubbers on 472 coal-burning power plants (the interventional units) in reducing Medicare hospitalizations among 22,603,597 Medicare beneficiaries residing across 23,675 ZIP codes in the United States (the outcome units). This is joint work with Corwin Zigler and Laura Forastiere.
Fabrizia Mealli is Professor of Statistics. Her research focuses on causal inference, program evaluation, estimation techniques, simulation methods, missing data, and Bayesian inference, with applications to the social and biomedical sciences. She held visiting positions at Harvard University, UCLA, LISER Luxembourg. She serves as coordinator of the Statistics track for the PhD program in Mathematics, Computer Science, Statistics of the University of Florence, and sits the Steering Committee of the European Causal Inference Meeting. She is Elected Fellow of the American Statistical Association, and currently an associate editor of “The Annals of Applied Statistics,” the “Journal of the American Statistical Association, T&M”, and “Observational Studies”.
The student session after the talk will allow students to ask Fabrizia questions about her research, the talk, the recommended paper or career opportunities. If you’re a student, make sure to register for this session.
This month’s paper is Identification and Estimation of Treatment and Interference Effects in Observational Studies on Networks by Laura Forastiere, Edoardo M. Airoldi, and Fabrizia Mealli. Causal inference under interference in network data is an important topic with many open issues.
Laura Forastiere, Edoardo M. Airoldi, and Fabrizia Mealli. Identification and Estimation of Treatment and Interference Effects in Observational Studies on Networks. Journal of the American Statistical Association, 0(0):1–18, June 2020. ISSN 0162-1459. Available at: https://doi.org/10.1080/01621459.2020.1768100.