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20232026
most citedConfounder-Dependent Bayesian Mixture Model: Characterizing Heterogeneity of Causal Effects in Air Pollution Epidemiology

1 citations · 1 across the 8 of their papers we have counts for

collaborators

9 papers

stat.ME2026

Bayesian Nonparametric Causal Inference for High-Dimensional Nutritional Data via Factor-Based Exposure Mapping

Dafne Zorzetto, Zizhao Xie, Julian Stamp +2

Diet plays a crucial role in health, and understanding the causal effects of dietary patterns is essential for informing public health policy and personalized nutrition strategies.…

stat.ME2025

Zero-Inflated Bayesian Multi-Study Infinite Non-Negative Matrix Factorization

Blake Hansen, Dafne Zorzetto, Valeria Edefonti +1

Understanding the association between dietary patterns and health outcomes, such as the cancer risk, is crucial to inform public health guidelines and shaping future dietary interv…

stat.ME2025

Causal Inference for Latent Outcomes Learned with Factor Models

Jenna M. Landy, Dafne Zorzetto, Roberta De Vito +1

In many fields$\unicode{x2013}$including genomics, epidemiology, natural language processing, social and behavioral sciences, and economics$\unicode{x2013}$it is increasingly impor…

stat.ME2025

Multivariate Causal Effects: a Bayesian Causal Regression Factor Model

Dafne Zorzetto, Jenna Landy, Corwin Zigler +2

The impact of wildfire smoke on air quality is a growing concern, contributing to air pollution through a complex mixture of chemical species with important implications for public…

stat.ME2024

Characterizing the Effects of Environmental Exposures on Social Mobility: Bayesian Semi-parametrics for Principal Stratification

Dafne Zorzetto, Paolo Dalla Torre, Sonia Petrone +2

Understanding the causal effects of air pollution exposures on social mobility is attracting increasing attention. At the same time, education is widely recognized as a key driver…

stat.ME2024

Sparse Bayesian Factor Models with Mass-Nonlocal Factor Scores

Yingjie Huang, Dafne Zorzetto, Roberta De Vito

Bayesian factor models are widely used for dimensionality reduction and pattern discovery in high-dimensional datasets across diverse fields. These models typically focus on imposi…