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main.R
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main.R
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rm(list = ls())
options(error = recover)
start_time = Sys.time()
library(data.table);
library(lubridate);
library(dplyr);
library(splines)
source("utils.R")
source("adr.R")
source("ipw.R")
source("qlearning.R")
source("aipw.R")
cluster = F
oracle_from_file = T # using pre-computed oracle/monte carlo rollout values for each policy in the policy class. Requires running run_oracle.R first
results_dir = "results"
plots_dir = "plots"
results_oracle_dir = "results_oracle"
log_trajs = F # log trajectories into RData files to produce state evolution plots later that compare Fitted-Q decision boundaries vs ADR
if (cluster) { # if running on a cluster
args=(commandArgs(T))
setup= as.character(args[1])
} else {
setup ="simu2" # simu1 corresponds to multiple-treatment setup (the second setup in the paper), and simu2 corresponds to the binary treatment setup (the first setup in the paper)
}
mu_learner="grf"
e_learner="grf"
d_learner="grf"
c_learner = "grf"
q_learner = "grf"
run_adr = T
run_ipw = T
run_aipw = F
run_q_learning = F # run Q-eval for each policy in the policy class
run_q_opt = T # run Q-Opt to find the best policy (fitted-Q iteration)
run_q_aipw_oracle = T # run AIPW and Q-eval for evaluating the oracle-found best policy
if (setup == "simu1") {
source("simu1.R")
e_rct=T
outcome_interest = "lifetime"
Q_max = T # Q_max is whether we take the max in fitted-Q (vs. take the min). e.g. are we maximizing or minimizing the outcome
treat_once = T
if (cluster) {
n = as.numeric(args[2])
} else {
n = 1000
}
n_oracle_eval = 20000
H=10
setup_params = list()
setup_params$z_prob = c(0.3, 0.3, 0.4)
if (cluster) {
setup_params$obs_noise = as.numeric(args[3])
REPIDX = as.numeric(args[4])
} else {
setup_params$obs_noise = 0
REPIDX = 1
}
params = get_policy_params()
nfold=5
foldid = get_foldid(n, nfold)
fnm = paste0(paste(paste0(results_dir, "/", setup), n, setup_params$obs_noise, REPIDX, sep="-"), ".csv")
print(fnm)
if (oracle_from_file) {
oracle_fnm = paste0(paste(paste0(results_oracle_dir, "/", setup), setup_params$obs_noise, sep="-"), ".csv")
}
data_res = get_data(n=n, H=H, setup_params, outcome_interest = outcome_interest, e_rct=e_rct, treat_once=treat_once)
} else if (setup == "simu2") {
source("simu2.R")
e_rct=F
treat_once = F
outcome_interest = "other"
Q_max = T
if (cluster) {
n = as.numeric(args[2])
} else {
n = 1000
}
n_oracle_eval = 20000
H = 10
setup_params = list()
if (cluster) {
setup_params$obs_noise = as.numeric(args[3])
setup_params$beta = as.numeric(args[4])
setup_params$sigma = as.numeric(args[5])
REPIDX = as.numeric(args[6])
} else {
setup_params$obs_noise = 0
setup_params$beta = 0.5
setup_params$sigma = 1
REPIDX = 1
}
if (log_trajs) {
cmp_fnm = paste(paste0(plots_dir, "/plot"), n, setup_params$obs_noise, setup_params$beta, setup_params$sigma, "cmp", REPIDX, sep="-")
}
params = get_policy_params()[1:3] # TODO change
nfold=5
foldid = get_foldid(n, nfold)
fnm = paste0(paste(paste0(results_dir, "/", setup), n, setup_params$obs_noise, setup_params$beta, setup_params$sigma, REPIDX, sep="-"), ".csv")
print(fnm)
oracle_fnm = paste0(paste(paste0(results_oracle_dir, "/", setup), setup_params$obs_noise, setup_params$beta, setup_params$sigma, sep="-"), ".csv")
data_res = get_data(n=n, H=H, setup_params, outcome_interest = outcome_interest, e_rct=e_rct, treat_once=treat_once)
}
data_static = data_res$data_static
data_dynamic = data_res$data_dynamic
censor_prob = data_res$censor_prob
n = dim(data_static)[1]
if (run_q_opt) {
if (setup == "simu1") {
normal_idx= which(data_static$patient_type == 2)
data_static_normal = data_static[normal_idx, -c("patient_type"), drop=F]
q_opt_res = qlearning(data_static_normal,
data_dynamic[data_dynamic$id %in% normal_idx,,drop=F],
H,
foldid[normal_idx],
treat_policy=NULL,
learner=q_learner,
q_opt = T,
Q_max = Q_max,
outcome_interest = outcome_interest)
} else {
q_opt_res = qlearning(data_static,
data_dynamic,
H,
foldid,
treat_policy=NULL,
learner=q_learner,
q_opt = T,
Q_max = Q_max,
outcome_interest = outcome_interest)
}
Q_hat_opt = q_opt_res$Q_hat
Q_regs_opt = q_opt_res$Q_reg_list
q_opt_eval_res = q_opt_eval(Q_regs_opt, n_oracle_eval, H, max(data_static$treatment_option), setup_params, outcome_interest, setup, treat_once, q_learner, Q_max)
q_opt_adv = q_opt_eval_res$V_pi - q_opt_eval_res$V_pi0
if (setup == "simu2" & log_trajs) {
q_Z = q_opt_eval_res$Z
q_A_time = q_opt_eval_res$A_time
eval_Z0 = q_opt_eval_res$Z0
eval_X0 = q_opt_eval_res$X0
}
} else {
q_opt_adv = 0
}
if (e_rct) {
e_hat = get_e_hat_rct(n, H, data_static)
} else {
e_hat = get_est(data_static, data_dynamic, foldid, H, regress_type="e", learner=e_learner)
}
traj_prob = get_traj_prob(e_hat, data_static$treat_start_time, data_static$treatment_option, H) # n by H+1
traj_prob_matrix = get_traj_prob_matrix(e_hat, data_static$treat_start_time, data_static$treatment_option, H) # n by H+1
if (run_adr) {
traj_after_prob_matrix = get_traj_after_prob_matrix(e_hat, data_static$treat_start_time,H) # n by H+1
if (setup == "simu1") {
die_idx = which(data_static$patient_type == 0)
live_idx = which(data_static$patient_type == 1)
normal_idx= which(data_static$patient_type == 2)
data_static_normal = data_static[normal_idx, -c("patient_type"), drop=F]
e_hat_normal = e_hat[normal_idx,,,drop=F]
mu_now = get_est(data_static_normal, data_dynamic[data_dynamic$id %in% normal_idx,,drop=F], foldid[normal_idx], H, regress_type="mu_now", learner=mu_learner, outcome_interest=outcome_interest)
mu_next_internal = get_est(data_static_normal, data_dynamic[data_dynamic$id %in% normal_idx,,drop=F], foldid[normal_idx], H, regress_type="mu_next_internal", learner=mu_learner, outcome_interest=outcome_interest, e_hat=e_hat_normal)
d_hat = get_est(data_static_normal, data_dynamic[data_dynamic$id %in% normal_idx,,drop=F], foldid[normal_idx], H, regress_type="d", learner=d_learner, outcome_interest = outcome_interest)
mu_next = get_mu_next(data_static_normal, mu_next_internal, d_hat, H, outcome_interest=outcome_interest)
mu_final = get_mu_final(die_idx, live_idx, normal_idx, mu_now, mu_next, mu_next_internal, H)
mu_now = mu_final$mu_now
mu_next = mu_final$mu_next
mu_next_internal = mu_final$mu_next_internal
} else {
mu_now = get_est(data_static, data_dynamic, foldid, H, regress_type="mu_now", learner=mu_learner, outcome_interest=outcome_interest)
mu_next_internal = get_est(data_static, data_dynamic, foldid, H, regress_type="mu_next_internal", learner=mu_learner, outcome_interest=outcome_interest, e_hat=e_hat)
d_hat = get_est(data_static, data_dynamic, foldid, H, regress_type="d", learner=d_learner, outcome_interest = outcome_interest)
mu_next = get_mu_next(data_static, mu_next_internal, d_hat, H, outcome_interest=outcome_interest)
}
adr_ret= adr(A_time = data_static$treat_start_time,
A_choice = data_static$treatment_option,
lifetime = data_static$total_months,
R = data_static$outcome,
mu_now = mu_now,
mu_next = mu_next,
mu_next_internal = mu_next_internal,
censor_prob = censor_prob,
traj_prob = traj_prob,
traj_after_prob_matrix = traj_after_prob_matrix)
}
num_choices = max(data_static$treatment_option)
if (run_aipw | run_q_learning | run_q_aipw_oracle) {
pi0_policy = list(month = rep(H+1, n),
choice = rep(1,n))
if (setup == "simu1") {
normal_idx= which(data_static$patient_type == 2)
die_idx = which(data_static$patient_type == 0)
live_idx = which(data_static$patient_type == 1)
data_static_normal = data_static[normal_idx, -c("patient_type"), drop=F]
pi0_policy_normal = list(month = pi0_policy$month[normal_idx],
choice = pi0_policy$choice[normal_idx])
q_opt_res = qlearning(data_static_normal,
data_dynamic[data_dynamic$id %in% normal_idx,,drop=F],
H,
foldid[normal_idx],
treat_policy=pi0_policy_normal,
learner=q_learner,
q_opt = F,
Q_max = Q_max,
outcome_interest = outcome_interest)
Q_hat_pi0 = array(NA, dim=c(n, H, num_choices+1))
Q_hat_pi0[die_idx,,] = 1
Q_hat_pi0[live_idx,,] = H+1
Q_hat_pi0[normal_idx,,] = q_opt_res$Q_hat
V_hat_pi0 = matrix(0, n, H)
V_hat_pi0[normal_idx,] = get_V_hat(Q_hat_pi0[normal_idx,,,drop=F], data_static$treat_start_time[normal_idx], data_static$treatment_option[normal_idx], pi0_policy$month[normal_idx], pi0_policy$choice[normal_idx], data_static$total_months[normal_idx], outcome_interest)
V_hat_pi0[die_idx,] = 1
V_hat_pi0[live_idx,] = H+1
} else {
q_opt_res_pi0 = qlearning(data_static,
data_dynamic,
H,
foldid,
treat_policy=pi0_policy,
learner=q_learner,
q_opt = F,
Q_max = Q_max,
outcome_interest = outcome_interest)
Q_hat_pi0 = q_opt_res_pi0$Q_hat
V_hat_pi0 = get_V_hat(Q_hat_pi0, data_static$treat_start_time, data_static$treatment_option, pi0_policy$month, pi0_policy$choice, data_static$total_months, outcome_interest)
}
if (run_aipw | run_q_aipw_oracle) {
aipw_pi0 = aipw(A_time= data_static$treat_start_time,
A_choice= data_static$treatment_option,
policy_time = rep(H+1, n),
policy_choice = rep(1, n),
R = data_static$outcome,
censor_prob = censor_prob,
traj_prob = traj_prob,
traj_prob_matrix = traj_prob_matrix,
Q_hat = Q_hat_pi0,
V_hat = V_hat_pi0,
weighted=F)
waipw_pi0 = aipw(A_time= data_static$treat_start_time,
A_choice= data_static$treatment_option,
policy_time = rep(H+1, n),
policy_choice = rep(1, n),
R = data_static$outcome,
censor_prob = censor_prob,
traj_prob = traj_prob,
traj_prob_matrix = traj_prob_matrix,
Q_hat = Q_hat_pi0,
V_hat = V_hat_pi0,
weighted=T)
}
if (run_q_learning | run_q_aipw_oracle) {
q_value_pi0 = mean(V_hat_pi0[,1])
}
}
if (oracle_from_file) {
oracle_matrix = matrix(as.vector(unlist(read.table(oracle_fnm, header=T))), 3,dim(params)[1]+1)
}
adv = sapply(1:dim(params)[1], function(pp_count) {
pp = params[pp_count,]
if (setup == "simu2" | setup == "simu1") {
if (oracle_from_file) {
adv_oracle = oracle_matrix[3,pp_count]
} else {
oracle = get_oracle_eval(n_oracle_eval, H, pp, setup_params, outcome_interest = outcome_interest, treat_once = treat_once)
adv_oracle = oracle$V_pi - oracle$V_pi0
}
}
treat_policy = get_treat_policy(data_static, data_dynamic, pp, H)
if (run_adr) {
adr_res = get_adr_value(adr_ret, treat_policy, data_static$total_months)
adv_adr = adr_res$adr
adv_adr_weighted = adr_res$adr_weighted
adv_adr_reg = adr_res$adr_reg
adv_adr_reg_weighted = adr_res$adr_reg_weighted
} else{
adv_adr = 0
adv_adr_weighted = 0
adv_adr_reg = 0
adv_adr_reg_weighted = 0
}
if (run_ipw) {
ipw_pi = ipw(A_time = data_static$treat_start_time,
A_choice = data_static$treatment_option,
policy_time = treat_policy$month,
policy_choice = treat_policy$choice,
R = data_static$outcome,
censor_prob = censor_prob,
traj_prob = traj_prob,
weighted=F)
ipw_pi0 = ipw(A_time = data_static$treat_start_time,
A_choice = data_static$treatment_option,
policy_time = rep(H+1, n),
policy_choice = rep(1, n),
R = data_static$outcome,
censor_prob = censor_prob,
traj_prob = traj_prob,
weighted=F)
adv_ipw = ipw_pi - ipw_pi0
wipw_pi = ipw(A_time = data_static$treat_start_time,
A_choice = data_static$treatment_option,
policy_time = treat_policy$month,
policy_choice = treat_policy$choice,
R = data_static$outcome,
censor_prob = censor_prob,
traj_prob = traj_prob,
weighted=T)
wipw_pi0 = ipw(A_time = data_static$treat_start_time,
A_choice = data_static$treatment_option,
policy_time = rep(H+1, n),
policy_choice = rep(1, n),
R = data_static$outcome,
censor_prob = censor_prob,
traj_prob = traj_prob,
weighted=T)
adv_ipw_weighted = wipw_pi - wipw_pi0
} else{
adv_ipw = 0
adv_ipw_weighted = 0
}
if (run_aipw | run_q_learning) {
if (setup == "simu1") {
treat_policy_normal = list(month = treat_policy$month[normal_idx],
choice = treat_policy$month[normal_idx])
q_opt_res = qlearning(data_static_normal,
data_dynamic[data_dynamic$id %in% normal_idx,,drop=F],
H,
foldid[normal_idx],
treat_policy=treat_policy_normal,
learner=q_learner,
q_opt = F,
Q_max = Q_max,
outcome_interest = outcome_interest)
Q_hat = array(NA, dim=c(n, H, num_choices+1))
Q_hat[die_idx,,] = 1
Q_hat[live_idx,,] = H+1
Q_hat[normal_idx,,] = q_opt_res$Q_hat
V_hat = matrix(0, n, H)
V_hat[normal_idx,] = get_V_hat(Q_hat[normal_idx,,,drop=F], data_static$treat_start_time[normal_idx], data_static$treatment_option[normal_idx], treat_policy$month[normal_idx], treat_policy$choice[normal_idx], data_static$total_months[normal_idx], outcome_interest)
V_hat[die_idx,] = 1
V_hat[live_idx,] = H+1
} else {
q_opt_res = qlearning(data_static,
data_dynamic,
H,
foldid,
treat_policy=treat_policy,
learner=q_learner,
q_opt = F,
Q_max = Q_max,
outcome_interest = outcome_interest)
Q_hat = q_opt_res$Q_hat
V_hat = get_V_hat(Q_hat, data_static$treat_start_time, data_static$treatment_option, treat_policy$month, treat_policy$choice, data_static$total_months, outcome_interest)
}
}
if (run_aipw) {
aipw_pi = aipw(A_time= data_static$treat_start_time,
A_choice= data_static$treatment_option,
policy_time = treat_policy$month,
policy_choice = treat_policy$choice,
R = data_static$outcome,
censor_prob = censor_prob,
traj_prob = traj_prob,
traj_prob_matrix = traj_prob_matrix,
Q_hat = Q_hat,
V_hat = V_hat,
weighted=F)
waipw_pi = aipw(A_time= data_static$treat_start_time,
A_choice= data_static$treatment_option,
policy_time = treat_policy$month,
policy_choice = treat_policy$choice,
R = data_static$outcome,
censor_prob = censor_prob,
traj_prob = traj_prob,
traj_prob_matrix = traj_prob_matrix,
Q_hat = Q_hat,
V_hat = V_hat,
weighted=T)
adv_aipw = aipw_pi - aipw_pi0
adv_aipw_weighted = waipw_pi - waipw_pi0
} else {
adv_aipw = 0
adv_aipw_weighted = 0
}
if (run_q_learning) {
q_value_pi = mean(V_hat[,1])
adv_q = q_value_pi - q_value_pi0
} else {
adv_q = 0
}
if (setup == "simu2" | setup == "simu1") {
c(adv_adr, adv_adr_reg, adv_adr_weighted, adv_adr_reg_weighted,
adv_ipw, adv_ipw_weighted,
adv_aipw, adv_aipw_weighted,
adv_q,
adv_oracle)
} else {
c(adv_adr, adv_adr_reg, adv_adr_weighted, adv_adr_reg_weighted,
adv_ipw, adv_ipw_weighted,
adv_aipw, adv_aipw_weighted,
adv_q)
}
})
if (setup == "simu2" | setup=="simu1") {
adv = matrix(unlist(adv), 10, dim(params)[1])
if (oracle_from_file) {
oracle_pp_idx = oracle_matrix[1, dim(oracle_matrix)[2]]
oracle_pp = params[oracle_pp_idx,]
if (!is.null(dim(oracle_pp))) {
if (dim(oracle_pp)[1] > 1){
oracle_pp = pp[1,]
}
}
oracle_best_value = oracle_matrix[3, dim(oracle_matrix)[2]]
} else {
oracle_pp_idx = which(adv[10,] == max(adv[10,]))
if (length(oracle_pp_idx) > 1) {
oracle_pp_idx = oracle_pp_idx[1]
}
oracle_pp = params[oracle_pp_idx,]
if (!is.null(dim(oracle_pp))) {
if (dim(oracle_pp)[1] > 1){
oracle_pp = pp[1,]
}
}
oracle_eval = get_oracle_eval(n_oracle_eval, H, oracle_pp, setup_params, outcome_interest = outcome_interest, treat_once = treat_once)
oracle_best_value = oracle_eval$V_pi - oracle_eval$V_pi0
adv[10,which.max(adv[10,])] = oracle_best_value # an independent run for oracle to avoid winner's curse
}
oracle_treat_policy = get_treat_policy(data_static, data_dynamic, oracle_pp, H)
if (log_trajs & setup == "simu2") {
wadr_pp = params[which(adv[3,] == max(adv[3,])),]
if (!is.null(dim(wadr_pp))) {
if (dim(wadr_pp)[1] > 1){
wadr_pp = wadr_pp[1,]
}
}
wipw_pp = params[which(adv[6,] == max(adv[6,])),]
if (!is.null(dim(wipw_pp))) {
if (dim(wipw_pp)[1] > 1){
wipw_pp = wipw_pp[1,]
}
}
adr_eval = get_oracle_eval(n = n_oracle_eval, H = H, param = wadr_pp, setup_params = setup_params, outcome_interest=outcome_interest, treat_once=treat_once, X0=eval_X0, Z0=eval_Z0)
ipw_eval = get_oracle_eval(n = n_oracle_eval, H = H, param = wipw_pp, setup_params = setup_params, outcome_interest=outcome_interest, treat_once=treat_once, X0=eval_X0, Z0=eval_Z0)
t_limit = max(max(q_A_time), max(adr_eval$policy$month), max(ipw_eval$policy$month))
save(list = ls(all.names = TRUE), file = paste0(cmp_fnm,".RData"), envir = environment())
}
if (run_q_aipw_oracle) {
if (setup == "simu1") {
oracle_treat_policy_normal = list(month=oracle_treat_policy$month[normal_idx],
choice=oracle_treat_policy$choice[normal_idx])
q_opt_res_oracle = qlearning(data_static_normal,
data_dynamic[data_dynamic$id %in% normal_idx,,drop=F],
H,
foldid[normal_idx],
treat_policy=oracle_treat_policy_normal,
learner=q_learner,
q_opt = F,
Q_max = Q_max,
outcome_interest = outcome_interest)
Q_hat_oracle = array(NA, dim=c(n, H, num_choices+1))
Q_hat_oracle[die_idx,,] = 1
Q_hat_oracle[live_idx,,] = H+1
Q_hat_oracle[normal_idx,,] = q_opt_res_oracle$Q_hat
V_hat_oracle = matrix(0, n, H)
V_hat_oracle[normal_idx,] = get_V_hat(Q_hat_oracle[normal_idx,,,drop=F], data_static$treat_start_time[normal_idx], data_static$treatment_option[normal_idx], oracle_treat_policy$month[normal_idx], oracle_treat_policy$choice[normal_idx], data_static$total_months[normal_idx], outcome_interest)
V_hat_oracle[die_idx,] = 1
V_hat_oracle[live_idx,] = H+1
} else {
q_opt_res_oracle = qlearning(data_static,
data_dynamic,
H,
foldid,
treat_policy=oracle_treat_policy,
learner=q_learner,
q_opt = F,
Q_max = Q_max,
outcome_interest = outcome_interest)
Q_hat_oracle = q_opt_res_oracle$Q_hat
V_hat_oracle = get_V_hat(Q_hat_oracle, data_static$treat_start_time, data_static$treatment_option, oracle_treat_policy$month, oracle_treat_policy$choice, data_static$total_months, outcome_interest)
}
aipw_pi_oracle = aipw(A_time= data_static$treat_start_time,
A_choice= data_static$treatment_option,
policy_time = oracle_treat_policy$month,
policy_choice = oracle_treat_policy$choice,
R = data_static$outcome,
censor_prob = censor_prob,
traj_prob = traj_prob,
traj_prob_matrix = traj_prob_matrix,
Q_hat = Q_hat_oracle,
V_hat = V_hat_oracle,
weighted=F)
waipw_pi_oracle = aipw(A_time= data_static$treat_start_time,
A_choice= data_static$treatment_option,
policy_time = oracle_treat_policy$month,
policy_choice = oracle_treat_policy$choice,
R = data_static$outcome,
censor_prob = censor_prob,
traj_prob = traj_prob,
traj_prob_matrix = traj_prob_matrix,
Q_hat = Q_hat_oracle,
V_hat = V_hat_oracle,
weighted=T)
adv_aipw_oracle = aipw_pi_oracle - aipw_pi0
adv_waipw_oracle = waipw_pi_oracle - waipw_pi0
q_value_pi_oracle = mean(V_hat_oracle[,1])
adv_q_oracle = q_value_pi_oracle - q_value_pi0
} else {
adv_aipw_oracle = 0
adv_waipw_oracle = 0
adv_q_oracle = 0
}
result = c(adr_best_value = adv[10,which.max(adv[1,])],
adr_reg_best_value = adv[10,which.max(adv[2,])],
adr_weighted_best_value = adv[10,which.max(adv[3,])],
adr_reg_weighted_best_value = adv[10,which.max(adv[4,])],
ipw_best_value = adv[10,which.max(adv[5,])],
ipw_weighted_best_value = adv[10,which.max(adv[6,])],
aipw_best_value = adv[10,which.max(adv[7,])],
aipw_weighted_best_value = adv[10,which.max(adv[8,])],
q_best_value = adv[10,which.max(adv[9,])],
oracle=oracle_best_value,
fitted_q = q_opt_adv,
adr_mse = mean((adv[1,] - adv[10,])^2),
adr_reg_mse = mean((adv[2,] - adv[10,])^2),
adr_weighted_mse = mean((adv[3,] - adv[10,])^2),
adr_reg_weighted_mse = mean((adv[4,] - adv[10,])^2),
ipw_mse = mean((adv[5,] - adv[10,])^2),
ipw_weighted_mse = mean((adv[6,] - adv[10,])^2),
aipw_mse = mean((adv[7,] - adv[10,])^2),
aipw_weighted_mse = mean((adv[8,] - adv[10,])^2),
q_mse = mean((adv[9,] - adv[10,])^2),
adv_adr_oracle = adv[1, oracle_pp_idx],
adv_adr_reg_oracle = adv[2, oracle_pp_idx],
adv_wadr_oracle = adv[3, oracle_pp_idx],
adv_wadr_reg_oracle = adv[4, oracle_pp_idx],
adv_ipw_oracle = adv[5, oracle_pp_idx],
adv_wipw_oracle = adv[6, oracle_pp_idx],
adv_aipw_oracle = adv_aipw_oracle,
adv_waipw_oracle= adv_waipw_oracle,
adv_q_oracle= adv_q_oracle)
write.table(result, fnm, row.names=FALSE)
} else {
stop("setup needs to be simu1 or simu2")
}
end_time = Sys.time()
print(end_time - start_time)