Our research group participated in the 21st Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2026), held in Rome from September 2 to 4, 2026.
During the conference, our PhD student Sergio Gaiotti presented the contribution “Treatment persistence drives estimator performance in longitudinal causal inference based on observational data: A simulation study”, authored by Sergio Gaiotti, Sara Poletto, Enrico Longato, Erica Tavazzi, and Martina Vettoretti.
The work, developed within the European REDDIE – Real-World Evidence for Decisions in Diabetes project, investigates how treatment dynamics influence the performance of causal inference methods for longitudinal observational data. In particular, the study highlights the role of treatment persistence in determining the behaviour of baseline and longitudinal estimators, showing that treatment dynamics can have a greater impact than functional complexity on estimator performance.
We thank the session chairs and the CIBB 2026 organisers for the stimulating discussion and for another great edition of a conference that has long been a regular appointment for our group.
