From the 1 of 5 linked papers with an AI index.
5 papers
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…
DCD-PFN: A Decoupling-Aware Foundation Model for Causal Discovery
Zhengkang Guan, Yikang Chen, Yi He +5
Causal discovery is critical for understanding complex data-generating mechanisms, yet traditional algorithms often struggle with highly non-linear and noisy systems, or suffer fro…
Causal Discovery via Quantile Partial Effect
Yikang Chen, Xingzhe Sun, Dehui Du
Quantile Partial Effect (QPE) is a statistic associated with conditional quantile regression, measuring the effect of covariates at different levels. Our theory demonstrates that w…
Exogenous Isomorphism for Counterfactual Identifiability
Yikang Chen, Dehui Du
This paper investigates -identifiability, a form of complete counterfactual identifiability within the Pearl Causal Hierarchy (PCH) framework, ensuring that a…
Exogenous Matching: Learning Good Proposals for Tractable Counterfactual Estimation
Yikang Chen, Dehui Du, Lili Tian
We propose an importance sampling method for tractable and efficient estimation of counterfactual expressions in general settings, named Exogenous Matching. By minimizing a common…