On Causal Identification under Markov Equivalence
On Causal Identification under Markov Equivalence
复制标题
论马尔可夫等价下的因果识别
DOI：
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发表时间：
2019
期刊：
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通讯作者：
E. Bareinboim
中科院分区：
文献类型：
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作者：
Amin Jaber;Jiji Zhang;E. Bareinboim
在这项工作中，我们研究了从观测分布和被调查领域因果结构的部分定性描述的组合计算实验分布的问题。这种描述是由一个部分祖先图（PAG）给出的，它代表了一个马尔可夫等价类的因果图，即在观测变量上包含相同的条件独立模型的图，并且可以从观测数据中学习。因此，我们开发了一个完整的算法来计算任意一组干预变量对任意结果集的因果效应。
In this work, we investigate the problem of computing an experimental distribution from a combination of the observational distribution and a partial qualitative description of the causal structure of the domain under investigation. This description is given by a partial ancestral graph (PAG) that represents a Markov equivalence class of causal diagrams, i.e., diagrams that entail the same conditional independence model over observed variables, and is learnable from the observational data. Accordingly, we develop a complete algorithm to compute the causal effect of an arbitrary set of intervention variables on an arbitrary outcome set.