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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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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