Stratified Fine Particulate Matter Exposure Response Functions (ERFs)

`Functions`

calibrate.R: Calibration function for estimating covariate balance weights with an entropy loss function.
gam_ipw.R: Functions for estimating exposure response function with generalized additive models and inverse probability weights. Includes a sandwich variance estimator for the point-wise confidence intervals of the ERF, which incorporates/propagates uncertainty from the fitted calibration weights.
gam_dr.R: A doubly-robust implementation of the GAM ERF estimate similar to the estimator in gam_ipw.R which incorporates an additional outcome nuisance model.

data_process.R: Data processing script which aggregates binary responses into strata within ZIP-code years.
desriptives.R: Descriptive statistics appearing in Tables 1 and 2 of the manuscript. Additional code for generating the covariate balance plots is also provided here.

models_ipw.R: Script that fits the strata-specific exposure response functions using estimated inverse probability weights. We use entropy balancing to estimate the IPWs - think of entropy balancing as a type of method of moments estimator whereas the more traditional way of estimating IPWs is with a plug-in estimator. Uses code from gam_ipw.R.
models_dr.R: A doubly-robust implementation to estimate the strata-specific exposure response curves. Uses code from gam_dr.R.
excess_deaths.R Predicts excess deaths attributable to heightened levels of PM2.5 from the fitted ERFs obtained in models_dr.R
excess_deaths.R Simulation study comparing the implementations featured in models_ipw.R and models_dr.R.

Plots/plot_main.R: Script for generating the main plots that appear in the manuscript.
Plots/plot_age.R: Script for generating the full age x race x dual Medicare/Medicaid eligibility curves with the associated strata-specific sample sizes.
Plots/plot_sens.R: Script for plotting sensitivity analysis results. Plots include a comparison of the overall ERF using an alternative exposure assessment.