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Arxiv.org
by Shu Yang; Jae Kwang Kim
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Predictive mean matching imputation is popular for handling item nonresponse in survey sampling. In this article, we study the asymptotic properties of the predictive mean matching estimator of the population mean. For variance estimation, the conventional bootstrap inference for matching estimators with fixed matches has been shown to be invalid due to the nonsmoothess nature of the matching estimator. We propose asymptotically valid replication variance estimation. The key strategy is to...
Topics: Statistics, Methodology
Source: http://arxiv.org/abs/1703.10256
Arxiv.org
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Propensity score weighting is a tool for causal inference to adjust for measured confounders in observational studies. In practice, data often present complex structures, such as clustering, which make propensity score modeling and estimation challenging. In addition, for clustered data, there may be unmeasured cluster-specific variables that are related to both the treatment assignment and the outcome. When such unmeasured cluster-specific confounders exist and are omitted in the propensity...
Topics: Statistics, Methodology
Source: http://arxiv.org/abs/1703.06086
Arxiv.org
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Causal inference with observational studies often relies on the assumptions of unconfoundedness and overlap of covariate distributions in different treatment groups. The overlap assumption is violated when some units have propensity scores close to zero or one, and therefore both theoretical and practical researchers suggest dropping units with extreme estimated propensity scores. We advance the literature in three directions. First, we clarify a conceptual issue of sample trimming by defining...
Topics: Statistics, Methodology
Source: http://arxiv.org/abs/1704.00666
Arxiv.org
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We consider causal inference from observational studies when confounders have missing values. When the confounders are missing not at random, causal effects are generally not identifiable. In this article, we propose a novel framework for nonparametric identification of causal effects with confounders missing not at random, but subject to instrumental missingness, that is, the missing data mechanism is independent of the outcome, given the treatment and possibly missing confounder values. We...
Topics: Statistics, Methodology
Source: http://arxiv.org/abs/1702.03951