MOCHA: Discovering Multi-Order Dynamic Causality in Temporal Point Processes

• URL: http://arxiv.org/abs/2508.18873v1
• Date: Tue, 26 Aug 2025 09:47:44 GMT
• Title: MOCHA: Discovering Multi-Order Dynamic Causality in Temporal Point Processes
• Authors: Yunyang Cao, Juekai Lin, Wenhao Li, Bo Jin,
• Abstract summary: MOCHA is a novel framework for discovering multi-order dynamic causality in temporal point processes.<n>We show that MOCHA achieves state-of-the-art performance in event prediction, and also reveals meaningful and interpretable causal structures.
• Score: 10.64307837085301
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Discovering complex causal dependencies in temporal point processes (TPPs) is critical for modeling real-world event sequences. Existing methods typically rely on static or first-order causal structures, overlooking the multi-order and time-varying nature of causal relationships. In this paper, we propose MOCHA, a novel framework for discovering multi-order dynamic causality in TPPs. MOCHA characterizes multi-order influences as multi-hop causal paths over a latent time-evolving graph. To model such dynamics, we introduce a time-varying directed acyclic graph (DAG) with learnable structural weights, where acyclicity and sparsity constraints are enforced to ensure structural validity. We design an end-to-end differentiable framework that jointly models causal discovery and TPP dynamics, enabling accurate event prediction and revealing interpretable structures. Extensive experiments on real-world datasets demonstrate that MOCHA not only achieves state-of-the-art performance in event prediction, but also reveals meaningful and interpretable causal structures.

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