ArXiv Preprint
Policymakers are required to evaluate the health benefits of reducing the
National Ambient Air Quality Standards (NAAQS; i.e., the safety standards) for
fine particulate matter PM 2.5 before implementing new policies. We formulate
this objective as a shift-response function (SRF) and develop methods to
analyze the problem using methods for causal inference, specifically under the
stochastic interventions framework. SRFs model the average change in an outcome
of interest resulting from a hypothetical shift in the observed exposure
distribution. We propose a new broadly applicable doubly-robust method to learn
SRFs using targeted regularization with neural networks. We evaluate our
proposed method under various benchmarks specific for marginal estimates as a
function of continuous exposure. Finally, we implement our estimator in the
motivating application that considers the potential reduction in deaths from
lowering the NAAQS from the current level of 12 $\mu g/m^3$ to levels that are
recently proposed by the Environmental Protection Agency in the US (10, 9, and
8 $\mu g/m^3$).
Mauricio Tec, Oladimeji Mudele, Kevin Josey, Francesca Dominici
2023-02-06
