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Causal latent Markov model for the comparison of multiple treatments in observational longitudinal studies

Francesco Bartolucci, Fulvia Pennoni and Giorgio Vittadini ()

MPRA Paper from University Library of Munich, Germany

Abstract: We extend to the longitudinal setting a latent class approach that has beed recently introduced by \cite{lanza:et:al:2013} to estimate the causal effect of a treatment. The proposed approach permits the evaluation of the effect of multiple treatments on subpopulations of individuals from a dynamic perspective, as it relies on a Latent Markov (LM) model that is estimated taking into account propensity score weights based on individual pre-treatment covariates. These weights are involved in the expression of the likelihood function of the LM model and allow us to balance the groups receiving different treatments. This likelihood function is maximized through a modified version of the traditional expectation-maximization algorithm, while standard errors for the parameter estimates are obtained by a non-parametric bootstrap method. We study in detail the asymptotic properties of the causal effect estimator based on the maximization of this likelihood function and we illustrate its finite sample properties through a series of simulations showing that the estimator has the expected behavior. As an illustration, we consider an application aimed at assessing the relative effectiveness of certain degree programs on the basis of three ordinal response variables when the work path of a graduate is considered as the manifestation of his/her human capital level across time.

Keywords: Causal inference; Expectation-Maximization algorithm; Hidden Markov models; Multiple treatments; Policy evaluation; Propensity score. (search for similar items in EconPapers)
JEL-codes: C1 C52 C53 C54 I23 J44 (search for similar items in EconPapers)
Date: 2015-08
New Economics Papers: this item is included in nep-ecm and nep-ore
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Journal Article: Causal Latent Markov Model for the Comparison of Multiple Treatments in Observational Longitudinal Studies (2016) Downloads
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