causal discovery 1directed acyclic graphs 1heterogeneous causal mechanisms 1tabular data 1transformer models 1
From the 1 of 3 linked papers with an AI index.
3 papers
cs.LG2026
DAG-FM: A Foundation Model for Causal Discovery under Heterogeneous Causal Mechanisms
Yikang Chen, Zhengkang Guan, Haoyuan Qian +5
The paper presents DAG-FM, a transformer‑based foundation model that discovers causal directed acyclic graphs from tabular data by sequentially predicting leaf and parent nodes and…
cs.LG2026
A Knowledge-Informed Pretrained Model for Causal Discovery
Wenbo Xu, Yue He, Yunhai Wang +4
Causal discovery has been widely studied, yet many existing methods rely on strong assumptions or fall into two extremes: either depending on costly interventional signals or parti…
stat.ML2026
Detecting Unobserved Confounders: A Kernelized Regression Approach
Yikai Chen, Yunxin Mao, Chunyuan Zheng +7
Detecting unobserved confounders is crucial for reliable causal inference in observational studies. Existing methods require either linearity assumptions or multiple heterogeneous…