Estimation of the average treatment effect when controlling for highdimensional 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 HighDimensional Confounders" <arXiv:2011.08661>. The package relies on the optimisation software 'MOSEK' <https://www.mosek.com/> which must be installed separately; see the documentation for 'Rmosek'.
Package details 


Author  Yuhao Wang [cre, aut], Rajen Shah [ctb] 
Maintainer  Yuhao Wang <yuhaow.thu@gmail.com> 
License  GPL3 
Version  0.1.0 
Package repository  View on CRAN 
Installation 
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