dipw.mean {dipw}  R Documentation 
Estimation of E[Y(1)] or E[Y(0)] from observational data
dipw.mean(
X,
Y,
W,
Treated = TRUE,
r = NULL,
kappa = 0.5,
splitting = c("1", "3", "random"),
B = 1,
...
)
X 
the n by p input covariance matrix 
Y 
the n dimensional observed response 
W 
the n dimensional binary vector indicating treatment assignment 
Treated 

r 
optional n dimensional vector containing initial estimates of
E[Y( 
kappa 
the weight parameter for quadratic programming. Default is 0.5 
splitting 
the options for splitting. "1" means B = 1 split, "3" means B = 3 splits, "random" means random splits. 
B 
the number of iterations for random splits, the default is 1. Only valid when splitting is set to "random". 
... 
additional arguments that can be passed to 
the expectation E[Y(1)] or E[Y(0)]
Wang, Y., Shah, R. D. (2020) Debiased inverse propensity score weighting for estimation of average treatment effects with highdimensional confounders https://arxiv.org/abs/2011.08661
## Not run:
# Estimating mean of the potential outcome with a toy data
# Notice that the external optimisation software \code{MOSEK}
# must be installed separately before running the example code.
# Without \code{MOSEK}, the example code is not executable.
# For how to install \code{MOSEK}, see documentation of \code{\link[Rmosek]{Rmosek}}.
set.seed(1)
n < 100; p < 200
X < scale(matrix(rnorm(n*p), n, p))
W < rbinom(n, 1, 1 / (1 + exp(X[, 1])))
Y < X[,1] + W * X[,2] + rnorm(n)
# Getting an estimate of potential outcome mean
(est < dipw.mean(X, Y, W, Treated=TRUE))
## End(Not run)