2 papers
stat.ML2026
Bayesian Causal Forests for Zero-Inflated Continuous Outcomes
Hugo Gobato Souto, Eric Wang, Igor Shuryak
Estimating (heterogeneous) treatment effects is still challenging when the outcome is semicontinuous (i.e., exhibiting both a discrete point mass at zero and a skewed continuous di…
cs.LG2025
CAST: Time-Varying Treatment Effects with Application to Chemotherapy and Radiotherapy on Head and Neck Squamous Cell Carcinoma
Everest Yang, Ria Vasishtha, Luqman K. Dad +7
Causal machine learning (CML) enables individualized estimation of treatment effects, offering critical advantages over traditional correlation-based methods. However, existing app…