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  "Title": "Debiased Inverse Propensity Score Weighting",
  "Description": "Estimation of the average treatment effect when\ncontrolling for high-dimensional confounders using debiased\ninverse propensity score weighting (DIPW). DIPW relies on the\npropensity score following a sparse logistic regression model,\nbut the regression curves are not required to be estimable.\nDespite this, our package also allows the users to estimate the\nregression curves and take the estimated curves as input to our\nmethods. Details of the methodology can be found in Yuhao Wang\nand Rajen D. Shah (2020) \"Debiased Inverse Propensity Score\nWeighting for Estimation of Average Treatment Effects with\nHigh-Dimensional Confounders\" <arXiv:2011.08661>. The package\nrelies on the optimisation software 'MOSEK'\n<https://www.mosek.com/> which must be installed separately;\nsee the documentation for 'Rmosek'.",
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  "Authors@R": "c(\nperson(\"Yuhao\", \"Wang\", email = \"yuhaow.thu@gmail.com\", role = c(\"cre\", \"aut\")),\nperson(\"Rajen\", \"Shah\", email = \"r.shah@statslab.cam.ac.uk\", role = \"ctb\"))",
  "Maintainer": "Yuhao Wang <yuhaow.thu@gmail.com>",
  "License": "GPL-3",
  "Encoding": "UTF-8",
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  "Packaged": {
    "Date": "2026-09-21 08:53:14 UTC",
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  "Author": "Yuhao Wang [cre, aut], Rajen Shah [ctb]",
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  "Date/Publication": "2020-11-30 08:00:05 UTC",
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