Latent Variables and Propensity Score Matching

Maciej Jakubowski

No 2010-06, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: This paper examines how including latent variables can benefit propensity score matching. A researcher can estimate, based on theoretical presumptions, the latent variable from the observed manifest variables and can use this estimate in propensity score matching. This paper demonstrates the benefits of such an approach and compares it with a method more common in econometrics, where the manifest variables are directly used in matching. We intuit that estimating the propensity score on the manifest variables introduces a measurement error that can be limited when estimating the propensity score on the estimated latent variable. We use Monte Carlo simulations to test how various matching methods behave under distinct circumstances found in practice. Also, we apply this approach to real data. Using the estimated latent variable in the propensity score matching increases the efficiency of treatment effect estimators. The benefits are larger for small samples, for non-linear processes, and for a large number of the manifest variables available, especially if they are highly correlated with the latent variable.

Keywords: factor analysis; latent variables; propensity score matching (search for similar items in EconPapers)
JEL-codes: C14 C15 C16 C31 C52 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2010
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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http://www.wne.uw.edu.pl/inf/wyd/WP/WNE_WP29.pdf First version, 2010 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2010-06

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