Zobrazeno 1 - 10
of 4 516
pro vyhledávání: '"doubly robust estimation"'
Autor:
Yu, Jiaxin, Qian, Tianchen
Micro-randomized trials (MRTs) are increasingly utilized for optimizing mobile health interventions, with the causal excursion effect (CEE) as a central quantity for evaluating interventions under policies that deviate from the experimental policy. H
Externí odkaz:
http://arxiv.org/abs/2411.10620
In clinical trials, the observation of participant outcomes may frequently be hindered by death, leading to ambiguity in defining a scientifically meaningful final outcome for those who die. Principal stratification methods are valuable tools for add
Externí odkaz:
http://arxiv.org/abs/2410.07483
We consider the problem of causal inference based on observational data (or the related missing data problem) with a binary or discrete treatment variable. In that context, we study inference for the counterfactual density functions and contrasts the
Externí odkaz:
http://arxiv.org/abs/2403.19917
Covariate imbalance between treatment groups makes it difficult to compare cumulative incidence curves in competing risk analyses. In this paper we discuss different methods to estimate adjusted cumulative incidence curves including inverse probabili
Externí odkaz:
http://arxiv.org/abs/2403.16256
In observational studies, covariates with substantial missing data are often omitted, despite their strong predictive capabilities. These excluded covariates are generally believed not to simultaneously affect both treatment and outcome, indicating t
Externí odkaz:
http://arxiv.org/abs/2402.14438
In cluster-randomized trials (CRTs), missing data can occur in various ways, including missing values in outcomes and baseline covariates at the individual or cluster level, or completely missing information for non-participants. Among the various ty
Externí odkaz:
http://arxiv.org/abs/2401.11278
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Suppose we are interested in the mean of an outcome that is subject to nonignorable nonresponse. This paper develops new semiparametric estimation methods with instrumental variables which affect nonresponse, but not the outcome. The proposed estimat
Externí odkaz:
http://arxiv.org/abs/2311.08691
In this paper we address the challenges posed by non-proportional hazards and informative censoring, offering a path toward more meaningful causal inference conclusions. We start from the marginal structural Cox model, which has been widely used for
Externí odkaz:
http://arxiv.org/abs/2311.07752
Akademický článek
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