7,822 research outputs found
Estimating propensity scores with missing covariate data using general location mixture models
In many observational studies, researchers estimate causal effects using propensity scores, e.g., by matching or sub-classifying on the scores. Estimation of propensity scores is complicated when some values of the covariates aremissing. We propose to use multiple imputation to create completed datasets, from which propensity scores can be estimated, with a general location mixture model. The model assumes that the control units are a latent mixture of (i)units whose covariates are drawn from the same distributions as the treated unitsā covariates and (ii) units whose covariates are drawn from different distributions. This formulation reduces the influence of control units outside the treated unitsā region of the covariate space on the estimation of parameters in the imputation model, which can result in more plausible imputations and better balance in the true covariate distributions. We illustrate the benefits of 1 the latent class modeling approach with simulations and with an observationalstudy of the effect of breast feeding on childrenās cognitive abilities
Estimating propensity scores with missing covariate data using general location mixture models
In many observational studies, researchers estimate causal effects using propensity scores, e.g., by matching or sub-classifying on the scores. Estimation of propensity scores is complicated when some values of the covariates aremissing. We propose to use multiple imputation to create completed datasets, from which propensity scores can be estimated, with a general location mixture model. The model assumes that the control units are a latent mixture of (i)units whose covariates are drawn from the same distributions as the treated unitsā covariates and (ii) units whose covariates are drawn from different distributions. This formulation reduces the influence of control units outside the treated unitsā region of the covariate space on the estimation of parameters in the imputation model, which can result in more plausible imputations and better balance in the true covariate distributions. We illustrate the benefits of 1 the latent class modeling approach with simulations and with an observationalstudy of the effect of breast feeding on childrenās cognitive abilities
Multiple imputation for sharing precise geographies in public use data
When releasing data to the public, data stewards are ethically and often
legally obligated to protect the confidentiality of data subjects' identities
and sensitive attributes. They also strive to release data that are informative
for a wide range of secondary analyses. Achieving both objectives is
particularly challenging when data stewards seek to release highly resolved
geographical information. We present an approach for protecting the
confidentiality of data with geographic identifiers based on multiple
imputation. The basic idea is to convert geography to latitude and longitude,
estimate a bivariate response model conditional on attributes, and simulate new
latitude and longitude values from these models. We illustrate the proposed
methods using data describing causes of death in Durham, North Carolina. In the
context of the application, we present a straightforward tool for generating
simulated geographies and attributes based on regression trees, and we present
methods for assessing disclosure risks with such simulated data.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS506 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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