6 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…
Semiparametric semi-supervised learning for general targets under distribution shift and decaying overlap
Lorenzo Testa, Qi Xu, Jing Lei +1
In modern scientific applications, large volumes of covariate data are readily available, while outcome labels are costly, sparse, and often subject to distribution shift. This asy…
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…
Efficient Difference-in-Differences Estimation when Outcomes are Missing at Random
Lorenzo Testa, Edward H. Kennedy, Matthew Reimherr
The Difference-in-Differences (DiD) method is a fundamental tool for causal inference, yet its application is often complicated by missing data. Although recent work has developed…
Towards Efficient Inference under Nonmonotone Missingness with General Imputation
Qi Xu, Lorenzo Testa, Jing Lei +1
Missing data are ubiquitous in classical survey and longitudinal studies as well as modern multi-modality data analysis. A longstanding challenge arises under nonmonotone missingne…