dipw
0.1.0Debiased Inverse Propensity Score Weighting
Overview
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'.
Install
Health
- NOTE r-devel-linux-x86_64-debian-clang
- NOTE r-devel-linux-x86_64-debian-gcc
- NOTE2026-03-1012 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
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- Documented parameters
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- Return-value docs
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Downloads
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Code & Tests
People & History
1 release. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.1.0Latest2026-03-10 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2020-11-30
- Total releases
- 1 / 6 yrs
- License
- GPL-3 OSI
- Download size
- 5.6 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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