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  "Title": "Propensity Score Weighting Methods for Dichotomous Treatments",
  "Description": "Provides propensity score weighting methods to control for\nconfounding in causal inference with dichotomous treatments and\ncontinuous/binary outcomes. It includes the following\nfunctional modules: (1) visualization of the propensity score\ndistribution in both treatment groups with mirror histogram,\n(2) covariate balance diagnosis, (3) propensity score model\nspecification test, (4) weighted estimation of treatment\neffect, and (5) augmented estimation of treatment effect with\noutcome regression. The weighting methods include the inverse\nprobability weight (IPW) for estimating the average treatment\neffect (ATE), the IPW for average treatment effect of the\ntreated (ATT), the IPW for the average treatment effect of the\ncontrols (ATC), the matching weight (MW), the overlap weight\n(OVERLAP), and the trapezoidal weight (TRAPEZOIDAL). Sandwich\nvariance estimation is provided to adjust for the sampling\nvariability of the estimated propensity score. These methods\nare discussed by Hirano et al (2003)\n<DOI:10.1111/1468-0262.00442>, Lunceford and Davidian (2004)\n<DOI:10.1002/sim.1903>, Li and Greene (2013)\n<DOI:10.1515/ijb-2012-0030>, and Li et al (2016)\n<DOI:10.1080/01621459.2016.1260466>.",
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  "Author": "Huzhang Mao <huzhangmao@gmail.com>, Liang Li\n<LLi15@mdanderson.org>",
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