3 papers
stat.ME2026
Towards a holistic understanding of Selection Bias for Causal Effect Identification
Yiwen Qiu, Filip KovaÄeviÄ, Shimeng Huang +2
Selection bias is pervasive in observational studies. For example, large scale biobanks data can exhibit ``healthy volunteer bias'' when respondents are healthier and of higher soc…
cs.CL2025
Prompting Fairness: Integrating Causality to Debias Large Language Models
Jingling Li, Zeyu Tang, Xiaoyu Liu +4
Large language models (LLMs), despite their remarkable capabilities, are susceptible to generating biased and discriminatory responses. As LLMs increasingly influence high-stakes d…
cs.LG2024
Choosing DAG Models Using Markov and Minimal Edge Count in the Absence of Ground Truth
Joseph D. Ramsey, Bryan Andrews, Peter Spirtes
We give a novel nonparametric pointwise consistent statistical test (the Markov Checker) of the Markov condition for directed acyclic graph (DAG) or completed partially directed ac…