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  "Description": "Targeted minimum loss-based estimators of counterfactual\nmeans and causal effects that are doubly-robust with respect\nboth to consistency and asymptotic normality (Benkeser et al\n(2017), <doi:10.1093/biomet/asx053>; MJ van der Laan (2014),\n<doi:10.1515/ijb-2012-0038>).",
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      "title": "drtmle: Doubly-Robust Inference in R",
      "author": "| David Benkeser | Emory University | Department of Biostatistics and Bioinformatics | 1518 Clifton Road, NE | Mailstop: 002-3AA | Atlanta, Georgia, 30322, U.S.A. | benkeser@emory.edu",
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        "Introduction \\label",
        "Definition and estimators of the average treatment effect",
        "Parameters of interest \\label",
        "G-computation estimators \\label",
        "Inverse probability of treatment estimators \\label",
        "Doubly-robust estimators \\label",
        "Doubly-robust inference \\label",
        "Outcome-adaptive propensity score estimation \\label",
        "Implementation of doubly-robust inference \\label",
        "Estimating regressions \\label",
        "Using glm to estimate regressions",
        "Using SuperLearner to estimate regressions",
        "Reducing dependence on random seed",
        "Outcome-adaptive propensity score",
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        "Partially cross-validated standard errors",
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        "Missing data \\label",
        "Multi-level treatments",
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        "Implementation of inference for adaptive IPTW",
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}