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dipw  

Debiased Inverse Propensity Score Weighting
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]
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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Other packages that cited dipw R package
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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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