2 citations · 2 across the 4 of their papers we have counts for
7 papers
Rescuing double robustness: safe estimation under complete misspecification
Lorenzo Testa, Francesca Chiaromonte, Kathryn Roeder
Double robustness is a major selling point of semiparametric and missing data methodology. Its virtues lie in protection against partial nuisance misspecification and asymptotic se…
RAwR: Role-Aware Rewiring via Approximate Equitable Partition
Riccardo Porcedda, Giuseppe Squillace, Bastian Epping +4
While Graph Neural Networks (GNNs) have demonstrated significant efficacy in node classification tasks, where predictions rely on local neighborhood information, the performance of…
GravityGraphSAGE: Link Prediction in Directed Attributed Graphs
Riccardo Porcedda, Francesca Chiaromonte, Fabrizio Lillo +1
Link prediction (inferring missing or future connections between nodes in a graph) is a fundamental problem in network science with widespread applications in, e.g., biological sys…
Doubly-Robust Functional Average Treatment Effect Estimation
Lorenzo Testa, Tobia Boschi, Francesca Chiaromonte +2
Understanding causal relationships in the presence of complex, structured data remains a central challenge in modern statistics and science in general. While traditional causal inf…
A Doubly Robust Machine Learning Approach for Disentangling Treatment Effect Heterogeneity with Functional Outcomes
Filippo Salmaso, Lorenzo Testa, Francesca Chiaromonte
Causal inference is paramount for understanding the effects of interventions, yet extracting personalized insights from increasingly complex data remains a significant challenge fo…
Sparse Bayesian Partially Identified Models for Sequence Count Data
Won Gu, Francesca Chiaromonte, Justin D. Silverman
In genomics, differential abundance and expression analyses are complicated by the compositional nature of sequence count data, which reflect only relative-not absolute-abundances…