[ABE-L] Seminários do Programa de Pós Graduação em Estatística/UFRGS –Extending Causal Mediation Analysis to Categorical Latent Variables

[Márcia Barbian]

Olá,

Temos o prazer de convidar a todas e todos para mais um seminário do
Programa de Pós-Graduação em Estatística da UFRGS.

Nosso próximo encontro ocorrerá de forma *REMOTA* no *dia 4 de agosto* e
terá como palestrante a pesquisadora Leila Amorim - Professora do
Departamento de Estatística da UFBA e Vice-Coordenadora do Programa de
Pós-Graduação em Estatística e Ciência de Dados (PGECD) da UFBA.
*Título:* Extending Causal Mediation Analysis to Categorical Latent
Variables

*Resumo:* Elucidating the mechanisms and causal pathways underlying
associations among variables is fundamental to the advancement of
scientific knowledge and the rigorous investigation of complex phenomena.
Within the causal mediation framework, the total effect of an intervention
on an outcome is typically decomposed into the natural direct effect (NDE)
and the natural indirect effect (NIE). While a substantial body of
literature addresses mediation analysis, comparatively little attention has
been given to settings in which key variables are latent and categorical,
as defined through latent class analysis (LCA). In this work, we extend
existing methodologies for the estimation of causal mediation effects by
explicitly incorporating latent components into the analytical framework.
The performance of the proposed approaches is evaluated via simulation
studies, encompassing both regression-based models and marginal structural
models, under scenarios in which the mediator and/or the outcome is latent.
An empirical application is provided using data from an intervention aimed
at promoting healthy behaviors among high school students in Brazil. The
proposed methodological developments have broad applicability and may be
readily extended to a wide range of substantive research contexts.


*Data: 4/08/2026*

*Horário: 13h30*
*Local: *https://mconf.ufrgs.br/webconf/ppgest

Abraço,

Márcia Barbian

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