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  "Title": "Sensitivity Assessment to Unmeasured Confounding with Multiple\nTreatments",
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  "Description": "A sensitivity analysis approach for unmeasured confounding\nin observational data with multiple treatments and a binary\noutcome. This approach derives the general bias formula and\nprovides adjusted causal effect estimates in response to\nvarious assumptions about the degree of unmeasured confounding.\nNested multiple imputation is embedded within the Bayesian\nframework to integrate uncertainty about the sensitivity\nparameters and sampling variability.  Bayesian Additive\nRegression Model (BART) is used for outcome modeling. The\ncausal estimands are the conditional average treatment effects\n(CATE) based on the risk difference.  For more details, see\npaper: Hu L et al. (2020) A flexible sensitivity analysis\napproach for unmeasured confounding with multiple treatments\nand a binary outcome with application to SEER-Medicare lung\ncancer data <arXiv:2012.06093>.",
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