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dipw
View on CRAN: Click
here
Download and install dipw package within the R console
Install from CRAN:
install.packages("dipw")
Install from Github:
library("remotes")
install_github("cran/dipw")
Install by package version:
library("remotes")
install_version("dipw", "0.1.0")
Attach the package and use:
library("dipw")
Maintained by
Yuhao Wang
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-11-30
Latest Update: 2020-11-30
Description:
Estimation of the average treatment effect when controlling for
high-dimensional confounders using debiased inverse propensity score
weighting (DIPW). DIPW relies on the propensity score following a sparse
logistic regression model, but the regression curves are not required to be
estimable. Despite this, our package also allows the users to estimate
the regression curves and take the estimated curves as input to our
methods. Details of the methodology can be found in Yuhao Wang and
Rajen D. Shah (2020) "Debiased Inverse Propensity Score Weighting for
Estimation of Average Treatment Effects with High-Dimensional Confounders"
. The package relies on the optimisation
software 'MOSEK' which must be installed separately;
see the documentation for 'Rmosek'.
How to cite:
Yuhao Wang (2020). dipw: Debiased Inverse Propensity Score Weighting. R package version 0.1.0, https://cran.r-project.org/web/packages/dipw. Accessed 21 Feb. 2025.
Previous versions and publish date:
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Complete documentation for dipw
Functions, R codes and Examples using
the dipw R package
Some associated functions: dipw.ate . dipw.mean .
Some associated R codes: dipw.R . Full dipw package functions and examples
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