6 papers · 1 filter
DAG-FM: A Foundation Model for Causal Discovery under Heterogeneous Causal Mechanisms
Yikang Chen, Zhengkang Guan, Haoyuan Qian +5
Causal discovery from observational tabular data remains fundamentally challenging, primarily due to the heterogeneity of underlying causal mechanisms and the high-dimensional comb…
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…
CIER: A Novel Experience Replay Approach with Causal Inference in Deep Reinforcement Learning
Jingwen Wang, Dehui Du, Yida Li +2
In the training process of Deep Reinforcement Learning (DRL), agents require repetitive interactions with the environment. With an increase in training volume and model complexity,…