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FIRB: Mixture and latent variable model for causal inference and analysis of socio-economic data

Project: Research


The research project concerns methodological and empirical developments about relevant classes of statistical models, which are formulated through mixtures of distributions or through latent variables/factors. As is well known, these approaches are strongly related and have great potential of application in several fields, where the characteristic of interest is not directly observable (e.g., quality of life, ability in a certain subject) or the differences in the behaviours of the statistical units depend on a form of heterogeneity that cannot be explained on the basis of the observed variables (unobservable heterogeneity).

The objective of the research project is, not only to propose advanced tools for the data analysis perspective, but also to develop new tools of causal inference and methods for the evaluation of the efficacy of policies or treatments. In this context, the project will mainly focus on the evaluation of healthcare or similar facilities, and on the performance evaluation of public intervention policies concerning education, health, juvenile crime and the youth labour market. These aims are in agreement with the objectives for a “better society” of Horizon 2020.

The research project will be focused on the following classes of models:

1. Generalized Linear Latent Variable Models (GLLVMs) and Item Response Theory (IRT) models: which are used when the response variables are of different nature and depend on variables of interest that are not directly observable; IRT models may be seen as particular GLLVMs which are suitable to the analysis of data deriving from the administration of test measuring a certain ability.
Effective start/end date1/10/1330/09/16
  • Bartolucci, Francesco (Principal Investigator)
  • Francis, Brian (Principal Investigator)
  • Pennoni, Fulvia (Principal Investigator)

Research outputs