2 citations · 2 across the 9 of their papers we have counts for
4 papers · 1 filter
Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Data
Gongxu Luo, Loka Li, Guangyi Chen +2
Interventional causal discovery seeks to identify causal relations by leveraging distributional changes introduced by interventions, even in the presence of latent confounders. Bey…
PersonaX: Multimodal Datasets with LLM-Inferred Behavior Traits
Loka Li, Wong Yu Kang, Minghao Fu +7
Understanding human behavior traits is central to applications in human-computer interaction, computational social science, and personalized AI systems. Such understanding often re…
When Selection Meets Intervention: Additional Complexities in Causal Discovery
Haoyue Dai, Ignavier Ng, Jianle Sun +5
We address the common yet often-overlooked selection bias in interventional studies, where subjects are selectively enrolled into experiments. For instance, participants in a drug…
Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders
Gongxu Luo, Haoyue Dai, Loka Li +3
Gene regulatory network inference (GRNI) aims to discover how genes causally regulate each other from gene expression data. It is well-known that statistical dependencies in observ…