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stat.ME2025
A Latent Causal Inference Framework for Ordinal Variables
Martina Scauda, Jack Kuipers, Giusi Moffa
Ordinal variables, such as on the Likert scale, are common in applied research. Yet, existing methods for causal inference tend to target nominal or continuous data. When applied t…
stat.ME2024
A new way to evaluate G-Wishart normalising constants via Fourier analysis
Ching Wong, Giusi Moffa, Jack Kuipers
The G-Wishart distribution is a core component for the Bayesian analysis of Gaussian graphical models as the conjugate prior for the precision matrix. Evaluating the marginal likel…
stat.ME2022★ 2 cited
The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventions
Jack Kuipers, Giusi Moffa
Describing the causal relations governing a system is a fundamental task in many scientific fields, ideally addressed by experimental studies. However, obtaining data under interve…