ITRFitInfer: This function implements the PEARL estimation for...

In muxuanliang/ITRInference: Estimation and inference of individualized treamtent rule using pooled-and-split de-correlated score

This function implements the PEARL estimation for individualized treatment rule and the inference procedure based on the pooled-and-split de-correlated score (see reference).

Description

This function implements the PEARL estimation for individualized treatment rule and the inference procedure based on the pooled-and-split de-correlated score (see reference).

Usage

ITRFitInfer(
  data,
  propensity = NULL,
  outcome = NULL,
  loss = "logistic",
  sampleSplitIndex = NULL,
  type.measure = "lossFun",
  outcomeModel = "kernel",
  outcomeFormula = NULL,
  propensityModel = "kernel",
  propensityFormula = NULL,
  intercept = FALSE,
  test = TRUE,
  indexToTest = c(1:8),
  parallel = FALSE,
  screeningMethod = "SIRS",
  outcomeScreeningFamily = "Gaussian",
  standardize = TRUE
)

Arguments

data	A list - list(predictor = x, treatment = trt, outcome = y)), where x is the covariate matrix, trt is 0 or 1 (1 indicates treatment), y is the outcome.
propensity	estimated propensity score p(trt=1|X). This should be used only when the propensity is estimated by a parametric model.
outcome	stimated outcome model E(y|trt, X). This should be used only when the outcome is estimated by a parametric model. It should be a list (list(control=..., treatment=...)).
outcomeModel	this selects method to estimate the outcome when outcome=NULL. Options include lm, glmnet, kernel, and gam. If lm is used, user also need to input the outcomeFormula like y~x used in lm. By default, kernel regression is selected.
propensityModel	Similar to outcomeModel.
intercept	includes intercept or not
test	whether return p-value and sd estimation.
indexToTest	indicates which coefficients to test. By default, c(1:8)
parallel	whether use parallel computing; by default, FALSE.
screeningMethod	when dimension is high, this selects method to preform variable screening for outcomeModel and propensityModel fitting. Methods in VariableScreening package are available.
standardize	whether standardize the input covariant matrix.

Value

fit is estimated coefficient for ITR on two split datasets. p-values are the p-value for each coefficients included in indexToTest. (betaAN-1.96*sigmaAN/sqrt(sample size), betaAN+1.96*sigmaAN/sqrt(sample size)) provides the 95% CI for the coefficients.

Author(s)

Muxuan Liang <[e-mail]>

References

Muxuan Liang, Young-Geun Choi, Yang Ning, Maureen Smith, Yingqi Zhao (2020). Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score.

Examples

# generate data
n <- 500
p <- 800
x <- array(rnorm(n*p), c(n,p))
beta_pi <- 0.4*c(1,1,1,0,-1,0,-1,rep(0, times=p-7))
trt <- apply(x, 1, function(t){ rbinom(1, 1, prob = exp(t[1]^2+t[2]^2+t[1]*t[2])/(1+exp(t[1]^2+t[2]^2+t[1]*t[2])))})
beta_main <- 0.8*c(-1,-1,1,-1,0,0,rep(0, times=p-6))
main_effect <- exp(x %*% beta_main)
beta_inter <- 0.7*c(1,1,-1,-1,rep(0, times=p-4))
interact_effect <- (pnorm(x %*% beta_inter)-0.5)*(abs(apply(x[,c(4:8)],1,sum)) + 4 * xi)
e <- rnorm(n)
y <- main_effect + sign(trt - 0.5) * interact_effect + e
# set up training data
dataTrain <- list(predictor = x, treatment = (trt > 0), outcome = y)
# fit our approach
infer_itr <- ITRFitInfer(data = dataTrain, outcomeModel = 'kernel', propensityModel = 'kernel')
# compare with q-learning and implement standard de-correlated score for q0learning
fit_qlearn <- QLearnFit(data = dataTrain, intercept = TRUE)
infer_qlearn <- QFitInfer(fit_qlearn, parallel=FALSE)

muxuanliang/ITRInference documentation built on Aug. 17, 2022, 6:03 p.m.