{"type":"ScholarlyArticle","title":"Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences","authors":[{"name":"Jie Qiao","slug":"jie-qiao","given":"Jie","family":"Qiao","affiliation":"School of Computer Science , Guangdong University of Technology , Guangzhou 510006 , China"},{"name":"Ruichu Cai","slug":"ruichu-cai","given":"Ruichu","family":"Cai","affiliation":"School of Computer Science , Guangdong University of Technology , Guangzhou 510006 , China; Peng Cheng Laboratory , Shenzhen 518066 , China"},{"name":"Siyu Wu","slug":"siyu-wu","given":"Siyu","family":"Wu","affiliation":"School of Computer Science , Guangdong University of Technology , Guangzhou 510006 , China"},{"name":"Yu Xiang","slug":"yu-xiang","given":"Yu","family":"Xiang","affiliation":"School of Computer Science , Guangdong University of Technology , Guangzhou 510006 , China"},{"name":"Keli Zhang","slug":"keli-zhang","given":"Keli","family":"Zhang","affiliation":"Huawei Noah's Ark Lab , Huawei , Shenzhen 518116 , China"},{"name":"Zhifeng Hao","slug":"zhifeng-hao","given":"Zhifeng","family":"Hao","affiliation":"College of Science , Shantou University , Shantou 515063 , China"}],"abstract":"Learning causal structure among event types from discrete-time event sequences is a particularly important but challenging task. Existing methods, such as the multivariate Hawkes processes based methods, mostly boil down to learning the so-called Granger causality which assumes that the cause event happens strictly prior to its effect event. Such an assumption is often untenable beyond applications, especially when dealing with discrete-time event sequences in low-resolution; and typical discrete Hawkes processes mainly suffer from identifiability issues raised by the instantaneous effect, i.e., the causal relationship that occurred simultaneously due to the low-resolution data will not be captured by Granger causality. In this work, we propose Structure Hawkes Processes (SHPs) that leverage the instantaneous effect for learning the causal structure among events type in discrete-time event sequence. The proposed method is featured with the Expectation-Maximization of the likelihood function and a sparse optimization scheme. Theoretical results show that the instantaneous effect is a blessing rather than a curse, and the causal structure is identifiable under the existence of the instantaneous effect. Experiments on synthetic and real-world data verify the effectiveness of the proposed method.","doi":"10.24963/ijcai.2023/633","url":"https://ijcai.org/proceedings/2023/633","pdf":"https://ijcai.org/proceedings/2023/0633.pdf","markdown":"https://ijcai.org/proceedings/2023/633.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/633","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Uncertainty in AI","track":"Main Track","number":633,"pages":{"first":5702,"last":5710},"keywords":["Uncertainty in AI: UAI: Causality, structural causal models and causal inference"],"videos":[],"references":[{"position":1,"raw":"[Achab et al., 2017] Massil Achab, Emmanuel Bacry, Stéphane Gaïffas, Iacopo Mastromatteo, and Jean-Franc ¸ois Muzy. 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