用扩充的因果网来识别随机干预的因果效应

Identifying the Causal Effects of Stochastic Interventions by Augmented Causal Networks

  • 摘要: 假设变量间的因果关系可以用一个因果网来表示. 为了识别随机干预的因果效应, 本文提出了随机干预下扩充的因果网. 接着, 我们给出由观测数据来识别随机干预因果效应的两个图准则. 当其中的某个准则满足时, 我们给出随机干预因果效应的一个简单的表达式, 该式便于研究者来评估随机干预的因果效应.

     

    Abstract: Suppose that the cause-effect relationships between variables can be described by a causal network. To identify the causal effect of a stochastic intervention, an augmented causal network for stochastic intervention is proposed in this paper. Then, we obtain two graphical criteria for identifying the causal effects of stochastic interventions from passive observations on observed variables only. When either of the two criteria is satisfied, a simple closed-form expression is provided for the causal effect of a stochastic intervention, which enables researchers to assess the causal effect with little effort.

     

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