Songyao Jin

According to our database, Songyao Jin authored at least 7 papers between 2024 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Causal Structure Learning in Hawkes Processes with Complex Latent Confounder Networks.

Songyao Jin, Biwei Huang

CoRR, August, 2025

Causal-Copilot: An Autonomous Causal Analysis Agent.

Xinyue Wang, Kun Zhou, Wenyi Wu, Har Simrat Singh, Fang Nan, Songyao Jin, Aryan Philip, Saloni Patnaik, Hou Zhu, Shivam Singh, Parjanya Prajakta Prashant, Qian Shen, Biwei Huang

CoRR, April, 2025

2024

On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factors.

Xinshuai Dong, Haoyue Dai, Yewen Fan, Songyao Jin, Sathyamoorthy Rajendran, Kun Zhang

CoRR, 2024

Identifying Latent State-Transition Processes for Individualized Reinforcement Learning.

Yuewen Sun, Biwei Huang, Yu Yao, Donghuo Zeng, Xinshuai Dong, Songyao Jin, Boyang Sun, Roberto Legaspi, Kazushi Ikeda, Peter Spirtes, Kun Zhang

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

On the Parameter Identifiability of Partially Observed Linear Causal Models.

Xinshuai Dong, Ignavier Ng, Biwei Huang, Yuewen Sun, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability.

Songyao Jin, Feng Xie, Guangyi Chen, Biwei Huang, Zhengming Chen, Xinshuai Dong, Kun Zhang

Proceedings of the Twelfth International Conference on Learning Representations, 2024

A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables.

Xinshuai Dong, Biwei Huang, Ignavier Ng, Xiangchen Song, Yujia Zheng, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang

Proceedings of the Twelfth International Conference on Learning Representations, 2024