Abstract An intervention may have an effect on units other than those to which it was administered. This phenomenon is called interference and it usually goes unmodeled. In this paper, we propose to combine Lauritzen-Wermuth-Frydenberg and Andersson-Madigan-Perlman chain graphs to create a new class of causal models that can represent both interference and non-interference relationships for Gaussian distributions. Specifically, we define the new class of models, introduce global and local and pairwise Markov properties for them, and prove their equivalence. We also propose an algorithm for maximum likelihood parameter estimation for the new models, and report experimental results. Finally, we show how to compute the effects of interventions in the new models.

中文翻译：

---

统一高斯 LWF 和 AMP 链图以模拟干扰

摘要 干预可能会对受其管理的单位以外的单位产生影响。这种现象称为干扰，它通常是未建模的。在本文中，我们建议结合 Lauritzen-Wermuth-Frydenberg 和 Andersson-Madigan-Perlman 链图来创建一类新的因果模型，该模型可以表示高斯分布的干扰和非干扰关系。具体来说，我们定义了新的模型类，为它们引入全局和局部以及成对马尔可夫属性，并证明它们的等价性。我们还提出了一种新模型的最大似然参数估计算法，并报告了实验结果。最后，我们展示了如何计算新模型中干预的效果。