F
Francesco Bartolucci
Researcher at University of Perugia
Publications - 225
Citations - 3077
Francesco Bartolucci is an academic researcher from University of Perugia. The author has contributed to research in topics: Latent class model & Expectation–maximization algorithm. The author has an hindex of 31, co-authored 214 publications receiving 2629 citations. Previous affiliations of Francesco Bartolucci include University of Urbino.
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Estimating dynamic causal effects with unobserved confounders: a latent class version of the inverse probability weighted estimator
TL;DR: In this article, the causal effect of a sequential binary treatment (typically corresponding to a policy or a subsidy in the economic context) on a final outcome, when the treatment assignment at a given occasion depends on the sequence of previous assignments as well as on time-varying confounders, is considered.
A latent Markov model from a new perspective with an application
TL;DR: The use of the latent Markov model is proposed in a context of the estimation of multiple causal effects when dealing with observational studies and there are unobserved baseline differences between individuals.
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Granger causality in dynamic binary short panel data models
TL;DR: In this article, the conditions for a logit model formulation that takes into account feedback effects without specifying a joint parametric model for the outcome and predetermined explanatory variables are provided. But their results hold for short panels with a large number of cross-section units, a case of great interest in microeconomic applications.
Inverse probability weighting to estimate causal effects of sequential treatments: a latent class extension to deal with unobserved confounding
TL;DR: In this article, the causal effect of a sequential treatment can be assessed via a MarginalStructural Model fitted by the Inverse ProbabilityWeighted (IPW) estimator, ex-tend the estimator to account for unobserved pre-treatment confounders, representing them by a discrete latent variable.
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Evaluation of student proficiency through a multidimensional finite mixture IRT model
TL;DR: In this paper, an approach for the evaluation in itinere of a student's proficiency accounting also for non-attempted exams is proposed, based on considering each exam as an item, so that responding to the item amounts to attempting the exam, and on an Item Response Theory model that includes two latent variables corresponding to the student's ability and the propensity to attempt the exam.