🤖 AI Summary
Existing methods predominantly rely on static or first-order causal assumptions, rendering them inadequate for capturing higher-order and time-varying dynamic causal dependencies in temporal point processes. To address this, we propose the first end-to-end differentiable framework that jointly models multi-order dynamic causal discovery and temporal point processes. Our approach introduces a learnable, time-varying directed acyclic graph (DAG) with parameterized edge weights, integrated into the intensity function parameterization; structural differentiability is ensured via sparsity and acyclicity constraints. By unifying gradient-based optimization with structural priors, the framework simultaneously learns both event generation mechanisms and latent time-evolving causal graphs. Extensive experiments on multiple real-world datasets demonstrate significant improvements in event prediction accuracy. Moreover, our method uncovers interpretable, higher-order causal pathways exhibiting temporal evolution—achieving both state-of-the-art predictive performance and strong causal interpretability.
📝 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.