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Statistical Methods and Applications

, Volume 18, Issue 2, pp 257–273 | Cite as

Estimating and using propensity score in presence of missing background data: an application to assess the impact of childbearing on wellbeing

  • Alessandra MatteiEmail author
Original Article

Abstract

Propensity score methods are an increasingly popular technique for causal inference. To estimate propensity scores, we must model the distribution of the treatment indicator given a vector of covariates. Much work has been done in the case where the covariates are fully observed. Unfortunately, many large scale and complex surveys, such as longitudinal surveys, suffer from missing covariate values. In this paper, we compare three different approaches and their underlying assumptions of handling missing background data in the estimation and use of propensity scores: a complete-case analysis, a pattern-mixture model based approach developed by Rosenbaum and Rubin (J Am Stat Assoc79:516–524, 1984), and a multiple imputation approach. We apply these methods to assess the impact of childbearing events on individuals’ wellbeing in Indonesia, using a sample of women from the Indonesia Family Life Survey.

Keywords

Ignorability Propensity score Missing data Childbearing Wellbeing 

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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  1. 1.Dipartimento di Statistica “G. Parenti”Università di FirenzeFirenzeItaly

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