建模事件序列中随时间变化的多阶因果关系，提升预测与可解释性。
MOCHA: Discovering Multi-Order Dynamic Causality in Temporal Point Processes
- 用动态有向无环图捕捉多跳因果路径，支持时变结构建模。
- 在真实数据集上实现最佳预测性能，同时揭示可解释的因果结构。
- 适合需要理解复杂事件因果机制的研究者与应用开发者。
发现时间点过程中的复杂因果依赖对建模现实世界事件序列至关重要。现有方法通常依赖静态或一阶因果结构，忽略了因果关系的多阶性和时变特性。本文提出MOCHA框架，用于发现时间点过程中的多阶动态因果关系。MOCHA将多阶影响建模为潜在时变图上的多跳因果路径。为建模此类动态，引入具有可学习结构权重的时变有向无环图（DAG），并通过约束保证无环性和稀疏性以确保结构有效性。设计了端到端可微框架，联合建模因果发现与时间点过程动态，实现精准事件预测并揭示可解释结构。在真实数据集上的大量实验表明，MOCHA不仅在事件预测上达到最先进水平，还能揭示有意义且可解释的因果结构。
原文摘要 · Abstract (English)
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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