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"
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"
arXiv link. The package relies on the optimisation
software MOSEK which must be installed separately;
see the documentation for Rmosek.
Once installed, please use ?dipw.ate and ?dipw.mean to check the user manual.
You can download this package via cran, for example using the R command install.packages("dipw") in your R console.