Description Usage Arguments Value References Examples
Estimation of E[Y(1)] or E[Y(0)] from observational data
1 2 3 4 5 6 7 8 9 10 11 |
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 high-dimensional confounders https://arxiv.org/abs/2011.08661
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ## 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)
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