Causal Latent Markov Model for the Comparison of Multiple Treatments in Observational Longitudinal Studies

Autor/inn/en	Bartolucci, Francesco; Pennoni, Fulvia; Vittadini, Giorgio
Titel	Causal Latent Markov Model for the Comparison of Multiple Treatments in Observational Longitudinal Studies
Quelle	In: Journal of Educational and Behavioral Statistics, 41 (2016) 2, S.146-179 (34 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998615622234
Schlagwörter	Causal Models; Markov Processes; Longitudinal Studies; Probability; Scores; Maximum Likelihood Statistics; Expectation; Computation; Mathematics; Error of Measurement; Nonparametric Statistics; Career Development; College Graduates; Human Capital; Regression (Statistics); Foreign Countries; Italy + Suchen Sie Ihr Suchwort? Kausalanalyse; Markowscher Prozess; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Expectancy; Erwartung; Mathematik; Messfehler; Berufsentwicklung; Hochschulabsolvent; Hochschulabsolventin; Humankapital; Regression; Regressionsanalyse; Ausland; Italien
Abstract	We extend to the longitudinal setting a latent class approach that was recently introduced by Lanza, Coffman, and Xu to estimate the causal effect of a treatment. The proposed approach enables an evaluation of multiple treatment effects 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 expectation-maximization 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 in which the work path of a graduate is considered as the manifestation of his or her human capital-level across time. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01