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Activity Number: 636 - Statistical Methods of Air Quality and Exposure
Type: Contributed
Date/Time: Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract #329810
Title: Wind as an Instrumental Variable in Air Pollution Epidemiology
Author(s): Keith Zirkle* and David C. Wheeler and Marie-Abele Bind
Companies: Virginia Commonwealth University and Virginia Commonwealth University and Harvard University
Keywords: interference; SUTVA; causal inference; Bayesian; instrumental variable; air pollution

Causal inference in air pollution epidemiology often violates the no interference assumption. No interference assumes that the outcome for any unit is independent of the treatment assigned for other units. In air pollution, this assumption does not hold when pollutants are carried downwind. Instrumental variables (IVs) are used in causal analysis for observational studies when treatment is not considered random. An IV is considered valid when (i) the IV can explain the treatment, e.g. air pollution, conditional on covariates and (ii) the IV is independent of the outcome conditional on covariates. We use incoming wind speed (WS) in areal units as an IV and propose a novel way to estimate direct and spillover effects of air pollution exposure on health outcomes based on compliance groups. We consider compliers to be units with high incoming WS and high pollution or low incoming WS and low pollution. Defiers are units with low incoming WS and high pollution or high incoming WS and low pollution. Direct and spillover effects can be estimated from within the compliers and defiers. We apply our Bayesian model to a 500 Cities dataset estimating the effects of particulate matter on asthma.

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

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