Public health interventions routinely target upstream determinants of health (e.g., social or environmental factors) to advance the health of populations. Even though such interventions are corner- stones of public health policy, methods for causal inference to evaluate their effectiveness are limited by a current focus on clinical investigations of individual-level therapies. One highly contentious example is the suite of reg- ulatory policies designed to reduce pollution-related health burden by limiting harmful emissions from US power plants. Unlike in clinical settings, comparing the effectiveness of these regulatory interventions is challenged by the fact that pollution emissions evolve throughout the atmosphere, rendering pollution and health outcomes at a given location determined in part by interventions taken at many power plants. A given unit's dependence on regulatory interventions at multiple power plants gives rise to what is known in the causal inference literature as interference. The fact that interventions are applied at one level of observation (e.g., power plants) and outcomes of interest are measured at another level (e.g., individuals or populations) presents a bipartite structure to the data. The combination of these features presents the challenge of bipartite causal inference with interference.
Aim 1 develops new Bayesian methods for bipartite partial interference in settings where observations can be clustered (e.g., by geography or pollution transport patterns) so that interference is present within cluster but not between clusters.
Aim 2 develops new Bayesian methods with general interference structures.
Aim 3 deploys our newly-developed methods to an unprecedented database on power plants, emissions, ambient air quality, and health outcomes across the entire US to compare the effectiveness of regulatory policies for reducing power plant emissions.
Aim 4 will support all other aims with the development of tools for reproducible research. The methods, data, and software we develop and disseminate will allow systematic and rigorous evaluation of the comparative effectiveness of complex public health interventions that exhibit interference among multiple levels of observational unit. The motivating example is air quality regulatory policy, but the methods will prove applicable to the evaluation of a variety of other types of complex public health interventions. The newly-developed methods will advance the ?eld of causal inference through relaxation of key assumptions that are routinely violated in prac- tice. Application of our methods to the evaluation of power plant regulations will provide the ?rst statistically-based evidence of the health impacts of such policies and constitute a paradigm shift in the way controversial air quality interventions are evaluated to support policy decisions.
Public health interventions routinely target upstream determinants of health (e.g., social or environmental factors) to advance the health of populations. Evaluating the effectiveness of such com- plex public health interventions cannot be accomplished with existing statistical tools, which have primarily been developed in clinical settings where treatments are applied directly to patients. We develop new statistical meth- ods for comparing the effectiveness of complex public health interventions and apply these methods to the eval- uation of contentious regulatory policies designed to reduce pollution-related health burden by limiting harmful emissions from US power plants.
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