Bipartite Interference and Air Pollution Transport: Estimating Health Effects of Power Plant Interventions

Abstract

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).

Methods for Assessing Spillover in Network-Based Studies of HIV/AIDS Prevention among People Who Use Drugs

“…When information on the true social relationships is only available for a subset of the sample, validation study/main study bias correction methods could be used to correct for the bias resulting from the definition of spillover sets based on the available information on randomization clusters or geographical distances. Another interesting future direction is the incorporation of edge weights into this method to reflect variations in the strength of connections relevant for spillover [ 162 , 163 ].…”

Spatial causal inference in the presence of unmeasured confounding and interference

“…Therefore, we assume that there exist potential outcomes Y i (z) for z ∈ Z n a vector of length n with exposure values on the n units. We reduce the number of possible potential outcomes by assuming the presence of a known interference network and exposure mapping [Aronow and Samii, 2017, Zigler et al, 2020, Forastiere et al, 2021. Let A denote a known adjacency matrix of dimension n × n, where A ij = 1 reflects that units i and j are connected, and A ij = 0 otherwise.…”

“…Interference has attracted a lot of attention in the last couple of decades [e.g. Sobel, 2006, Hudgens and Halloran, 2008, Manski, 2013, Aronow and Samii, 2017, among many others] with some studies that focus explicitly on how interference manifests in spatial settings [Verbitsky-Savitz and Raudenbush, 2012, Wang et al, 2020, Zigler et al, 2020, Antonelli and Beck, 2020, Giffin et al, 2022.…”