dipw.ate {dipw}

Estimate the Average treatment effect E[Y(1) - Y(0)] from observational data

Description

Estimate the Average treatment effect E[Y(1) - Y(0)] from observational data

Usage

dipw.ate(
  X,
  Y,
  W,
  r1 = NULL,
  r0 = 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

r1

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

r0

optional n dimensional vector of an initial estimate of E[Y(0) | 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 used when splitting is set to "random".

...

additional arguments that can be passed to cv.glmnet

Value

tau the estimated average treatment effect

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 average treatment effect 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 average treatment effect
(est <- dipw.ate(X, Y, W))

## End(Not run)