Gaussian Processes for Estimating Causal Exposure Response Curves.

Gaussian processes for the estimation of causal exposure-response curves (GP-CERF)

Summary

Gaussian Process (GP) and nearest neighbor Gaussian Process (nnGP) approaches for nonparametric modeling.

Installation

library("devtools")
install_github("NSAPH-Software/GPCERF", ref="develop")
library("GPCERF")

Usage

Note: The following examples will also need installing ranger R package.

GP

library(GPCERF)
set.seed(781)
sim_data <- generate_synthetic_data(sample_size = 500, gps_spec = 1)

n_core <- 1

m_xgboost <- function(nthread = n_core, ...) {
  SuperLearner::SL.xgboost(nthread = nthread, ...)
}

m_ranger <- function(num.threads = n_core, ...){
  SuperLearner::SL.ranger(num.threads = num.threads, ...)
}

# Estimate GPS function
gps_m <- estimate_gps(cov_mt = sim_data[,-(1:2)],
                      w_all = sim_data$treat,
                      sl_lib = c("m_xgboost", "m_ranger"),
                      dnorm_log = TRUE)

# exposure values
q1 <- stats::quantile(sim_data$treat, 0.05)
q2 <- stats::quantile(sim_data$treat, 0.95)

w_all <- seq(q1, q2, 1)

params_lst <- list(alpha = 10 ^ seq(-2, 2, length.out = 10),
                   beta = 10 ^ seq(-2, 2, length.out = 10),
                   g_sigma = c(0.1, 1, 10),
                   tune_app = "all")

cerf_gp_obj <- estimate_cerf_gp(sim_data,
                                w_all,
                                gps_m,
                                params = params_lst,
                                outcome_col = "Y",
                                treatment_col = "treat",
                                covariates_col = paste0("cf", seq(1,6)),
                                nthread = n_core)
summary(cerf_gp_obj)
plot(cerf_gp_obj)

GPCERF standard Gaussian grocess exposure response function object

Optimal hyper parameters(#trial: 300): 
  alpha = 12.9154966501488   beta = 12.9154966501488   g_sigma = 0.1

Optimal covariate balance: 
  cf1 = 0.069 
  cf2 = 0.082 
  cf3 = 0.063 
  cf4 = 0.066 
  cf5 = 0.056 
  cf6 = 0.081

Original covariate balance: 
  cf1 = 0.222 
  cf2 = 0.112 
  cf3 = 0.175 
  cf4 = 0.318 
  cf5 = 0.198 
  cf6 = 0.257
            ----***----      

nnGP

set.seed(781)
sim_data <- generate_synthetic_data(sample_size = 5000, gps_spec = 1)

m_xgboost <- function(nthread = 12, ...) {
  SuperLearner::SL.xgboost(nthread = nthread, ...)
}

m_ranger <- function(num.threads = 12, ...){
  SuperLearner::SL.ranger(num.threads = num.threads, ...)
}

# Estimate GPS function
gps_m <- estimate_gps(cov_mt = sim_data[,-(1:2)],
                      w_all = sim_data$treat,
                      sl_lib = c("m_xgboost", "m_ranger"),
                      dnorm_log = TRUE)

# exposure values
q1 <- stats::quantile(sim_data$treat, 0.05)
q2 <- stats::quantile(sim_data$treat, 0.95)

w_all <- seq(q1, q2, 1)


params_lst <- list(alpha = 10 ^ seq(-2, 2, length.out = 10),
                   beta = 10 ^ seq(-2, 2, length.out = 10),
                   g_sigma = c(0.1, 1, 10),
                   tune_app = "all",
                   n_neighbor = 50,
                   block_size = 1e3)

cerf_nngp_obj <- estimate_cerf_nngp(sim_data,
                                    w_all,
                                    gps_m,
                                    params = params_lst,
                                    outcome_col = "Y",
                                    treatment_col = "treat",
                                    covariates_col = paste0("cf", seq(1,6)),
                                    nthread = 12)
summary(cerf_nngp_obj)
plot(cerf_nngp_obj)

GPCERF nearest neighbore Gaussian process exposure response function object summary

Optimal hyper parameters(#trial: 300): 
  alpha = 0.0278255940220712   beta = 0.215443469003188   g_sigma = 0.1

Optimal covariate balance: 
  cf1 = 0.062 
  cf2 = 0.070 
  cf3 = 0.091 
  cf4 = 0.062 
  cf5 = 0.076 
  cf6 = 0.088

Original covariate balance: 
  cf1 = 0.115 
  cf2 = 0.137 
  cf3 = 0.145 
  cf4 = 0.296 
  cf5 = 0.208 
  cf6 = 0.225
            ----***----                       

Code of Conduct

Please note that the GPCERF project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Contributing

Contributions to the package are encouraged. For detailed information on how to contribute, please refer to the CONTRIBUTING guidelines.

Reporting Issues & Seeking Support

If you encounter any issues with GPCERF, we kindly ask you to report them on our GitHub by opening a new issue. To expedite resolution, including a reproducible example is highly appreciated. For those seeking assistance or further details about a particular topic, feel free to initiate a Discussion on GitHub or open an issue. Additionally, for more direct inquiries, the package maintainer can be reached via the email address provided in the DESCRIPTION file.

References

Ren, B., Wu, X., Braun, D., Pillai, N. and Dominici, F., 2021. Bayesian modeling for exposure response curve via gaussian processes: Causal effects of exposure to air pollution on health outcomes. arXiv preprint doi:10.48550/arXiv.2105.03454.