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

TL;DR

MOCHA is a novel framework that discovers multi-order, time-varying causal relationships in temporal point processes, enhancing event prediction accuracy and interpretability.

Contribution

It introduces a dynamic causal discovery method using a time-varying DAG with learnable weights, capturing complex multi-order influences in TPPs.

Findings

Achieves state-of-the-art event prediction performance

Reveals meaningful and interpretable causal structures

Effectively models multi-hop causal paths over a latent graph

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.