The University of Southampton
University of Southampton Institutional Repository

Estimating propensity scores with missing covariate data using general location mixture models

Mitra, Robin and Reiter, Jerome P. (2009) Estimating propensity scores with missing covariate data using general location mixture models , Southampton, UK Southampton Statistical Sciences Reseach Institute 47pp. (S3RI Methodology working papers, M09/13).

Record type: Monograph (Working Paper)

Abstract

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 are
missing. 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 units’ covariates and (ii) units whose covariates are drawn from different distributions. This formulation reduces the influence of control units outside the treated units’ 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 observational
study of the effect of breast feeding on children’s cognitive abilities.

PDF s3ri-workingpaper-M09-13.pdf - Other
Download (550kB)

More information

Published date: 4 August 2009
Keywords: latent class, missing data, multiple imputation, observational studies, propensity score

Identifiers

Local EPrints ID: 67154
URI: http://eprints.soton.ac.uk/id/eprint/67154
PURE UUID: 9669b728-280e-4205-94bb-9f3b7f5f15c2

Catalogue record

Date deposited: 06 Aug 2009
Last modified: 19 Jul 2017 00:20

Export record

Contributors

Author: Robin Mitra
Author: Jerome P. Reiter

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×