From the 1 of 12 linked papers with an AI index.
10 papers · 1 filter
IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning
Zefeng Liang, Jie Qiao, Ruichu Cai +2
Model-based reinforcement learning (MBRL), which learns environment dynamics to generate synthetic experience, is a promising approach to sample-efficient decision making. Numerous…
CDFM: Towards a General-Purpose Causal Discovery Foundation Model
Jie Qiao, Ruichu Cai, Zijian Li +6
The paper proposes CDFM, a foundation model trained on synthetic causal graphs that can infer causal structures in a zero‑shot manner across diverse domains, using a variational fr…
Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption
Weilin Chen, Ruichu Cai, Jie Qiao +2
Estimating causal effects under networked interference from observational data is a crucial yet challenging problem. Most existing methods mainly rely on the networked unconfounded…
Long-term Causal Inference via Modeling Sequential Latent Confounding
Weilin Chen, Ruichu Cai, Yuguang Yan +2
Long-term causal inference is an important but challenging problem across various scientific domains. To solve the latent confounding problem in long-term observational studies, ex…
Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights
Zeqin Yang, Weilin Chen, Ruichu Cai +7
Long-term treatment effect estimation is a significant but challenging problem in many applications. Existing methods rely on ideal assumptions, such as no unobserved confounders o…
Horizontal and Vertical Federated Causal Structure Learning via Higher-order Cumulants
Wei Chen, Wanyang Gu, Linjun Peng +3
Federated causal discovery aims to uncover the causal relationships between entities while protecting data privacy, which has significant importance and numerous applications in re…