dipw.mean {dipw}

Estimation of E[Y(1)] or E[Y(0)] from observational data

Description

Estimation of E[Y(1)] or E[Y(0)] from observational data

Usage

dipw.mean(
  X,
  Y,
  W,
  Treated = TRUE,
  r = NULL,
  kappa = 0.5,
  splitting = c("1", "3", "random"),
  B = 1,
  ...
)

Arguments

X

the n by p input covariance matrix

Y

the n dimensional observed response

W

the n dimensional binary vector indicating treatment assignment

Treated

TRUE if we seek to estimate E[Y(1)], FALSE if we instead wish to estimate E[Y(0)]. The default is TRUE

r

optional n dimensional vector containing initial estimates of E[Y(Treated) | X_i] for i = 1, ..., n. The default is NULL

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 cv.glmnet

Value

the expectation E[Y(1)] or E[Y(0)]

References

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

Examples

## 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)