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Propensity score weighting is a tool for causal inference to adjust for measured confounders. Survey data are often collected under complex sampling designs such as multistage cluster sampling, which presents challenges for propensity score modeling and estimation. In addition, for clustered data, there may also be unobserved cluster effects related to both the treatment and the outcome. When such unmeasured confounders exist and are omitted in the propensity score model, the subsequent...
Topics: Methodology, Statistics
Source: http://arxiv.org/abs/1607.07521
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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Estimation of the covariance structure of spatial processes is of fundamental importance in spatial statistics. In the literature, several non-parametric and semi-parametric methods have been developed to estimate the covariance structure based on the spectral representation of covariance functions. However,they either ignore the high frequency properties of the spectral density, which are essential to determine the performance of interpolation procedures such as Kriging, or lack of theoretical...
Topics: Methodology, Statistics
Source: http://arxiv.org/abs/1508.06886
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
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
by Shu Yang; Jae Kwang Kim
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Multiple imputation is a popular imputation method for general purpose estimation. Rubin(1987) provided an easily applicable formula for the variance estimation of multiple imputation. However, the validity of the multiple imputation inference requires the congeniality condition of Meng(1994), which is not necessarily satisfied for method of moments estimation. This paper presents the asymptotic bias of Rubin's variance estimator when the method of moments estimator is used as a complete-sample...
Topics: Methodology, Statistics
Source: http://arxiv.org/abs/1508.06977
Coarse Structural Nested Mean Models (SNMMs) provide useful tools to estimate treatment effects from longitudinal observational data with time-dependent confounders. Coarse SNMMs lead to a large class of estimators,within which an optimal estimator can be derived under the conditions of well-specified models for the treatment effect, for treatment initiation, and for nuisance regression outcomes (Lok & Griner, 2015). The key assumption lies in a well-specified model for the treatment...
Topics: Methodology, Statistics
Source: http://arxiv.org/abs/1508.06867
Arxiv.org
by Shu Yang; Jae Kwang Kim
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Fractional imputation (FI) is a relatively new method of imputation for handling item nonresponse in survey sampling. In FI, several imputed values with their fractional weights are created for each missing item. Each fractional weight represents the conditional probability of the imputed value given the observed data, and the parameters in the conditional probabilities are often computed by an iterative method such as EM algorithm. The underlying model for FI can be fully parametric,...
Topics: Methodology, Statistics
Source: http://arxiv.org/abs/1508.06945
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
Arxiv.org
by Shu Yang; Guido W. Imbens; Zhanglin Cui; Douglas Faries; Zbigniew Kadziola
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In this paper, we develop new methods for estimating average treatment effects in observational studies, focusing on settings with more than two treatment levels under unconfoundedness given pre-treatment variables. We emphasize subclassification and matching methods which have been found to be effective in the binary treatment literature and which are among the most popular methods in that setting. Whereas the literature has suggested that these particular propensity-based methods do not...
Topics: Methodology, Statistics
Source: http://arxiv.org/abs/1508.06948