Bartolucci, Francesco and Pennoni, Fulvia and Vittadini, Giorgio (2015): Causal latent Markov model for the comparison of multiple treatments in observational longitudinal studies.

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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 pretreatment 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 expectationmaximization algorithm, while standard errors for the parameter estimates are obtained by a nonparametric 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.
Item Type:  MPRA Paper 

Original Title:  Causal latent Markov model for the comparison of multiple treatments in observational longitudinal studies 
English Title:  Causal latent Markov model for the comparison of multiple treatments in observational longitudinal studies 
Language:  English 
Keywords:  Causal inference, ExpectationMaximization algorithm, Hidden Markov models, Multiple treatments, Policy evaluation, Propensity score. 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C52  Model Evaluation, Validation, and Selection C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C53  Forecasting and Prediction Methods ; Simulation Methods C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C54  Quantitative Policy Modeling I  Health, Education, and Welfare > I2  Education and Research Institutions > I23  Higher Education ; Research Institutions J  Labor and Demographic Economics > J4  Particular Labor Markets > J44  Professional Labor Markets ; Occupational Licensing 
Item ID:  66492 
Depositing User:  Prof. Fulvia Pennoni 
Date Deposited:  08 Sep 2015 14:53 
Last Modified:  28 Sep 2019 22:53 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/66492 