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Copy pathcontinuous_X_sim_choose_stopping_k.R
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continuous_X_sim_choose_stopping_k.R
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source('./src/dependencies.R')
params_save_name <- "2,8,32,64,128"
load(paste0("./params/simulation_parameters_continuous_", params_save_name, ".RData"))
simulation_parameters_continuous$n <- 5000
simulation_parameters_continuous$reps <- 200
simulation_parameters_continuous$save_name <- params_save_name
simulation_parameters_continuous$num_cores <- 10
with(simulation_parameters_continuous, {
if (num_cores != 1) {
cl <- makeCluster(num_cores); cl_stop <- TRUE
clusterEvalQ(cl, {
library(tidyverse)
library(SMUT)
library(splines)
library(fda)
library(gbm)
library(ranger)
library(caret)
library(glmnet)
library(pbapply)
})
} else {cl <- 1; cl_stop <- FALSE}
sim_out <- pbsapply (1:reps, function(rep) {
source('./src/estimation_functions.R')
##########################################################
## Simulate estimation sample
##########################################################
Z1 = runif(n, 0, 1); Z2 = runif(n, 0, 1); Z3 = runif(n, 0, 1); Z4 = runif(n, 0, 1); Z5 = runif(n, 0, 1)
Z6 = runif(n, 0, 1); Z7 = runif(n, 0, 1); Z8 = runif(n, 0, 1); Z9 = runif(n, 0, 1); Z10 = runif(n, 0, 1)
sim_data_est <- create_b_spline_basis(
data=data.frame(Z1),
continuous_vars=c('Z1'),
knots=true_knots,
boundary_knots=list(c(0,1)),
polynomial_degree=3,
degree_of_interactions=1
)
p_X = sim_data_est %*% theta_X
p_Y = sim_data_est %*% theta_Y
X = rbinom(n, 1, p_X)
Y = rbinom(n, 1, p_Y)
sim_data_est <- data.frame(Z1=Z1, Z2=Z2, Z3=Z3, Z4=Z4, Z5=Z5, Z6=Z6, Z7=Z7, Z8=Z8, Z9=Z9, Z10=Z10, X=X, Y=Y)
basis_est <- c(
lapply(1:length(K_b_splines), function(k) {
create_b_spline_basis(
data = sim_data_est %>% dplyr::select(-c(X,Y)),
continuous_vars = names(sim_data_est %>% dplyr::select(-c(X,Y))),
knots = knots[[k]], boundary_knots = boundary_knots[[k]],
degree_of_interactions = degree_of_interactions[k],
polynomial_degree = polynomial_degree[k]
)
}),
lapply(1:length(K_fourier), function(k) {
create_fourier_basis(
data = sim_data_est %>% dplyr::select(-c(X,Y)),
continuous_vars = names(sim_data_est %>% dplyr::select(-c(X,Y))),
period = periods[k],
nbasis = K_fourier[k],
degree_of_interactions = degree_of_interactions[k]
)
})
)
sigma_est_eff1 <- c('est' = compute_sigma(basis = basis_est,
trt = sim_data_est$X))
sigma_est_eff0 <- c('est' = compute_sigma(basis = basis_est,
trt = 1-sim_data_est$X))
##########################################################
## compute estimates of the nuisance parameters in estimation data
##########################################################
prob_outcome1_trt1_stacked_classifier <- estimate_prob_stacked_classifier(covariates_df = sim_data_tr %>% dplyr::select(-c(Y)),
label_vector = sim_data_tr %>% dplyr::select(Y) %>% {.[[1]]},
params_boosted_tree = params_boosted_tree_outcome,
params_random_forest = params_random_forest_outcome,
params_knn = params_knn_outcome,
params_lasso = params_lasso_outcome,
params_glm = params_glm_outcome,
meta_model = meta_model_outcome$meta_model,
meta_model_formula = meta_model_outcome$meta_model_formula,
predict_data = sim_data_est %>% dplyr::select(-c(Y)) %>% mutate(X = 1))
prob_outcome1_trt0_stacked_classifier <- estimate_prob_stacked_classifier(covariates_df = sim_data_tr %>% dplyr::select(-c(Y)),
label_vector = sim_data_tr %>% dplyr::select(Y) %>% {.[[1]]},
params_boosted_tree = params_boosted_tree_outcome,
params_random_forest = params_random_forest_outcome,
params_knn = params_knn_outcome,
params_lasso = params_lasso_outcome,
params_glm = params_glm_outcome,
meta_model = meta_model_outcome$meta_model,
meta_model_formula = meta_model_outcome$meta_model_formula,
predict_data = sim_data_est %>% dplyr::select(-c(Y)) %>% mutate(X = 0))
prob_outcome1_trt1_boosted_tree <- estimate_prob_individual_model(covariates_df = sim_data_tr %>% dplyr::select(-c(Y)),
label_vector = sim_data_tr %>% dplyr::select(Y) %>% {.[[1]]},
params = params_boosted_tree_outcome,
model = "boosted_tree",
predict_data = sim_data_est %>% dplyr::select(-c(Y)) %>% mutate(X = 1))
prob_outcome1_trt0_boosted_tree <- estimate_prob_individual_model(covariates_df = sim_data_tr %>% dplyr::select(-c(Y)),
label_vector = sim_data_tr %>% dplyr::select(Y) %>% {.[[1]]},
params = params_boosted_tree_outcome,
model = "boosted_tree",
predict_data = sim_data_est %>% dplyr::select(-c(Y)) %>% mutate(X = 0))
prob_outcome1_trt1_random_forest <- estimate_prob_individual_model(covariates_df = sim_data_tr %>% dplyr::select(-c(Y)),
label_vector = sim_data_tr %>% dplyr::select(Y) %>% {.[[1]]},
params = params_random_forest_outcome,
model = "random_forest",
predict_data = sim_data_est %>% dplyr::select(-c(Y)) %>% mutate(X = 1))
prob_outcome1_trt0_random_forest <- estimate_prob_individual_model(covariates_df = sim_data_tr %>% dplyr::select(-c(Y)),
label_vector = sim_data_tr %>% dplyr::select(Y) %>% {.[[1]]},
params = params_random_forest_outcome,
model = "random_forest",
predict_data = sim_data_est %>% dplyr::select(-c(Y)) %>% mutate(X = 0))
prob_outcome1_trt1_knn <- estimate_prob_individual_model(covariates_df = sim_data_tr %>% dplyr::select(-c(Y)),
label_vector = sim_data_tr %>% dplyr::select(Y) %>% {.[[1]]},
params = params_knn_outcome,
model = "knn",
predict_data = sim_data_est %>% dplyr::select(-c(Y)) %>% mutate(X = 1))
prob_outcome1_trt0_knn <- estimate_prob_individual_model(covariates_df = sim_data_tr %>% dplyr::select(-c(Y)),
label_vector = sim_data_tr %>% dplyr::select(Y) %>% {.[[1]]},
params = params_knn_outcome,
model = "knn",
predict_data = sim_data_est %>% dplyr::select(-c(Y)) %>% mutate(X = 0))
prob_outcome1_trt1_lasso <- estimate_prob_individual_model(covariates_df = sim_data_tr %>% dplyr::select(-c(Y)),
label_vector = sim_data_tr %>% dplyr::select(Y) %>% {.[[1]]},
params = params_lasso_outcome,
model = "lasso",
predict_data = sim_data_est %>% dplyr::select(-c(Y)) %>% mutate(X = 1))
prob_outcome1_trt0_lasso <- estimate_prob_individual_model(covariates_df = sim_data_tr %>% dplyr::select(-c(Y)),
label_vector = sim_data_tr %>% dplyr::select(Y) %>% {.[[1]]},
params = params_lasso_outcome,
model = "lasso",
predict_data = sim_data_est %>% dplyr::select(-c(Y)) %>% mutate(X = 0))
prob_outcome1_trt1_glm <- estimate_prob_individual_model(covariates_df = sim_data_tr %>% dplyr::select(-c(Y)),
label_vector = sim_data_tr %>% dplyr::select(Y) %>% {.[[1]]},
params = params_glm_outcome,
model = "glm",
predict_data = sim_data_est %>% dplyr::select(-c(Y)) %>% mutate(X = 1))
prob_outcome1_trt0_glm <- estimate_prob_individual_model(covariates_df = sim_data_tr %>% dplyr::select(-c(Y)),
label_vector = sim_data_tr %>% dplyr::select(Y) %>% {.[[1]]},
params = params_glm_outcome,
model = "glm",
predict_data = sim_data_est %>% dplyr::select(-c(Y)) %>% mutate(X = 0))
##########################################################
## compute IF22 for choosing the stopping value of k with Gram matrix computed from
# 1) large separate sample (oracle), and
# 2) estimation sample
##########################################################
# a_resid is multiplied by -1 to correct for the fact that Sigma here is defined as A*t(basis)%*%basis rather than the -A*t(basis)%*%basis
# which is in expected in the functions used to compute IF22 for the treatment effect parameters
HOIF_stacked_eff1_outcome <- compute_HOIF_sequence(
a_resid = compute_resid(trt = -sim_data_est$X, outcome = sim_data_est$Y, pred = prob_outcome1_trt1_stacked_classifier, type = 'outcome'),
y_resid = compute_resid(trt = sim_data_est$X, outcome = sim_data_est$Y, pred = prob_outcome1_trt1_stacked_classifier, type = 'outcome'),
basis = rep(basis_est, 2),
sigma = c(sigma_oracle_eff1, sigma_est_eff1),
num_cores = 1
)
HOIF_stacked_eff0_outcome <- compute_HOIF_sequence(
a_resid = compute_resid(trt = -(1-sim_data_est$X), outcome = sim_data_est$Y, pred = prob_outcome1_trt0_stacked_classifier, type = 'outcome'),
y_resid = compute_resid(trt = 1-sim_data_est$X, outcome = sim_data_est$Y, pred = prob_outcome1_trt0_stacked_classifier, type = 'outcome'),
basis = rep(basis_est, 2),
sigma = c(sigma_oracle_eff0, sigma_est_eff0),
num_cores = 1
)
HOIF_boosted_tree_eff1_outcome <- compute_HOIF_sequence(
a_resid = compute_resid(trt = -sim_data_est$X, outcome = sim_data_est$Y, pred = prob_outcome1_trt1_boosted_tree, type = 'outcome'),
y_resid = compute_resid(trt = sim_data_est$X, outcome = sim_data_est$Y, pred = prob_outcome1_trt1_boosted_tree, type = 'outcome'),
basis = rep(basis_est, 2),
sigma = c(sigma_oracle_eff1, sigma_est_eff1),
num_cores = 1
)
HOIF_boosted_tree_eff0_outcome <- compute_HOIF_sequence(
a_resid = compute_resid(trt = -(1-sim_data_est$X), outcome = sim_data_est$Y, pred = prob_outcome1_trt0_boosted_tree, type = 'outcome'),
y_resid = compute_resid(trt = 1-sim_data_est$X, outcome = sim_data_est$Y, pred = prob_outcome1_trt0_boosted_tree, type = 'outcome'),
basis = rep(basis_est, 2),
sigma = c(sigma_oracle_eff0, sigma_est_eff0),
num_cores = 1
)
HOIF_random_forest_eff1_outcome <- compute_HOIF_sequence(
a_resid = compute_resid(trt = -sim_data_est$X, outcome = sim_data_est$Y, pred = prob_outcome1_trt1_random_forest, type = 'outcome'),
y_resid = compute_resid(trt = sim_data_est$X, outcome = sim_data_est$Y, pred = prob_outcome1_trt1_random_forest, type = 'outcome'),
basis = rep(basis_est, 2),
sigma = c(sigma_oracle_eff1, sigma_est_eff1),
num_cores = 1
)
HOIF_random_forest_eff0_outcome <- compute_HOIF_sequence(
a_resid = compute_resid(trt = -(1-sim_data_est$X), outcome = sim_data_est$Y, pred = prob_outcome1_trt0_random_forest, type = 'outcome'),
y_resid = compute_resid(trt = 1-sim_data_est$X, outcome = sim_data_est$Y, pred = prob_outcome1_trt0_random_forest, type = 'outcome'),
basis = rep(basis_est, 2),
sigma = c(sigma_oracle_eff0, sigma_est_eff0),
num_cores = 1
)
HOIF_knn_eff1_outcome <- compute_HOIF_sequence(
a_resid = compute_resid(trt = -sim_data_est$X, outcome = sim_data_est$Y, pred = prob_outcome1_trt1_knn, type = 'outcome'),
y_resid = compute_resid(trt = sim_data_est$X, outcome = sim_data_est$Y, pred = prob_outcome1_trt1_knn, type = 'outcome'),
basis = rep(basis_est, 2),
sigma = c(sigma_oracle_eff1, sigma_est_eff1),
num_cores = 1
)
HOIF_knn_eff0_outcome <- compute_HOIF_sequence(
a_resid = compute_resid(trt = -(1-sim_data_est$X), outcome = sim_data_est$Y, pred = prob_outcome1_trt0_knn, type = 'outcome'),
y_resid = compute_resid(trt = 1-sim_data_est$X, outcome = sim_data_est$Y, pred = prob_outcome1_trt0_knn, type = 'outcome'),
basis = rep(basis_est, 2),
sigma = c(sigma_oracle_eff0, sigma_est_eff0),
num_cores = 1
)
HOIF_lasso_eff1_outcome <- compute_HOIF_sequence(
a_resid = compute_resid(trt = -sim_data_est$X, outcome = sim_data_est$Y, pred = prob_outcome1_trt1_lasso, type = 'outcome'),
y_resid = compute_resid(trt = sim_data_est$X, outcome = sim_data_est$Y, pred = prob_outcome1_trt1_lasso, type = 'outcome'),
basis = rep(basis_est, 2),
sigma = c(sigma_oracle_eff1, sigma_est_eff1),
num_cores = 1
)
HOIF_lasso_eff0_outcome <- compute_HOIF_sequence(
a_resid = compute_resid(trt = -(1-sim_data_est$X), outcome = sim_data_est$Y, pred = prob_outcome1_trt0_lasso, type = 'outcome'),
y_resid = compute_resid(trt = 1-sim_data_est$X, outcome = sim_data_est$Y, pred = prob_outcome1_trt0_lasso, type = 'outcome'),
basis = rep(basis_est, 2),
sigma = c(sigma_oracle_eff0, sigma_est_eff0),
num_cores = 1
)
HOIF_glm_eff1_outcome <- compute_HOIF_sequence(
a_resid = compute_resid(trt = -sim_data_est$X, outcome = sim_data_est$Y, pred = prob_outcome1_trt1_glm, type = 'outcome'),
y_resid = compute_resid(trt = sim_data_est$X, outcome = sim_data_est$Y, pred = prob_outcome1_trt1_glm, type = 'outcome'),
basis = rep(basis_est, 2),
sigma = c(sigma_oracle_eff1, sigma_est_eff1),
num_cores = 1
)
HOIF_glm_eff0_outcome <- compute_HOIF_sequence(
a_resid = compute_resid(trt = -(1-sim_data_est$X), outcome = sim_data_est$Y, pred = prob_outcome1_trt0_glm, type = 'outcome'),
y_resid = compute_resid(trt = 1-sim_data_est$X, outcome = sim_data_est$Y, pred = prob_outcome1_trt0_glm, type = 'outcome'),
basis = rep(basis_est, 2),
sigma = c(sigma_oracle_eff0, sigma_est_eff0),
num_cores = 1
)
list(
HOIF_stacked_eff1_outcome = HOIF_stacked_eff1_outcome,
HOIF_stacked_eff0_outcome = HOIF_stacked_eff0_outcome,
HOIF_boosted_tree_eff1_outcome = HOIF_boosted_tree_eff1_outcome,
HOIF_boosted_tree_eff0_outcome = HOIF_boosted_tree_eff0_outcome,
HOIF_random_forest_eff1_outcome = HOIF_random_forest_eff1_outcome,
HOIF_random_forest_eff0_outcome = HOIF_random_forest_eff0_outcome,
HOIF_knn_eff1_outcome = HOIF_knn_eff1_outcome,
HOIF_knn_eff0_outcome = HOIF_knn_eff0_outcome,
HOIF_lasso_eff1_outcome = HOIF_lasso_eff1_outcome,
HOIF_lasso_eff0_outcome = HOIF_lasso_eff0_outcome,
HOIF_glm_eff1_outcome = HOIF_glm_eff1_outcome,
HOIF_glm_eff0_outcome = HOIF_glm_eff0_outcome
)
}, cl=cl)
if(cl_stop) {stopCluster(cl)}
save(sim_out, file=paste0("./output/continuous_sim_choose_stopping_k_",
simulation_parameters_continuous$n, "_",
simulation_parameters_continuous$reps, "_",
simulation_parameters_continuous$save_name,
".RData"))
})