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Statistical Laboratory

Martina Scauda

I am a PhD student supervised by Qingyuan Zhao. My current research focuses on causal inference and its applications in empirical legal studies.

Publications

A Latent Causal Inference Framework for Ordinal Variables
M Scauda, J Kuipers, G Moffa
(2025)
(link to publication)
Nonlinear mixed-effects modeling as a method for causal inference to predict exposures under desired within-subject dose titration schemes
C Bartels, M Scauda, N Coello, T Dumortier, B Bornkamp, G Moffa
– CPT: pharmacometrics & systems pharmacology
(2024)
14,
68
(doi: 10.1002/psp4.13239)
Correcting for confounding in longitudinal experiments: positioning non-linear mixed effects modeling as implementation of standardization using latent conditional exchangeability
C Bartels, M Scauda, N Coello, T Dumortier, B Bornkamp, G Moffa
(2024)
(link to publication)
Physics-Informed Machine Learning Simulator for Wildfire Propagation
L Bottero, F Calisto, G Graziano, V Pagliarino, M Scauda, S Tiengo, S Azeglio
(2020)
(link to publication)

Frontpage talks

30
Apr
16:30 - 18:00: Statistics Clinic Easter 2025 I
Cambridge Statistics Clinic
MR5 at the CMS

Research Group

Statistical Laboratory

Email

ms2985@cam.ac.uk

Room

D0.04

Personal homepage

https://martinascauda.github.io

    Study at Cambridge

    About the University

    Research at Cambridge