An intervention may have an effect on units other than those to which the intervention 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 interference relationships. Specifically, we define the new class of models, introduce global and local and pairwise Markov properties for them, and prove their equivalence.
翻译:干预可能会对干预实施对象以外的单位产生影响。 这种现象被称为干扰, 通常不进行改造。 在本文中, 我们提议将劳里琴- 韦尔穆斯- 弗莱登贝格 和安德森- 马迪冈- 佩尔曼 链条图结合起来, 以创建能够代表干扰关系的新型因果模型。 具体地说, 我们定义了新型模型, 为他们引入了全球、 本地和对称的马可夫特性, 并证明它们具有等同性 。