MOCHA: Discovering Multi-Order Dynamic Causality in Temporal Point Processes
This addresses the need for modeling multi-order and time-varying causal relationships in event sequences, which is incremental as it builds on existing TPP methods by introducing dynamic causality discovery.
The paper tackles the problem of discovering complex causal dependencies in temporal point processes, which are often overlooked by existing methods that rely on static or first-order structures, and proposes MOCHA, a framework that achieves state-of-the-art performance in event prediction while revealing interpretable causal structures.
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.