3 papers
cs.AI2026
Heterogeneous Causal Discovery of Repeated Undesirable Health Outcomes
Shishir Adhikari, Guido Muscioni, Mark Shapiro +2
Understanding the factors that trigger or prevent undesirable health outcomes across patient subpopulations is essential for designing targeted interventions. While randomized cont…
cs.LG2025
Relational Causal Discovery with Latent Confounders
Matteo Negro, Andrea Piras, Ragib Ahsan +2
Estimating causal effects from real-world relational data can be challenging when the underlying causal model and potential confounders are unknown. While several causal discovery…
cs.AI2025
Learning Exposure Mapping Functions for Inferring Heterogeneous Peer Effects
Shishir Adhikari, Sourav Medya, Elena Zheleva
In causal inference, interference refers to the phenomenon in which the actions of peers in a network can influence an individual's outcome. Peer effect refers to the difference in…