dipw

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" <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'.

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Maintainer: ‘Yuhao Wang <yuhaow.thu@gmail.com>’

The Description field contains
  High-Dimensional Confounders" <arXiv:2011.08661>. The package relies on
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘Yuhao Wang <yuhaow.thu@gmail.com>’

The Description field contains
  High-Dimensional Confounders" <arXiv:2011.08661>. The package relies on
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

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Maintainer: ‘Yuhao Wang <yuhaow.thu@gmail.com>’

The Description field contains
  High-Dimensional Confounders" <arXiv:2011.08661>. The package relies on
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘Yuhao Wang <yuhaow.thu@gmail.com>’

The Description field contains
  High-Dimensional Confounders" <arXiv:2011.08661>. The package relies on
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

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