Separators and Adjustment Sets in Causal Graphs: Complete Criteria and an Algorithmic Framework
Separators and Adjustment Sets in Causal Graphs: Complete Criteria and an Algorithmic Framework
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DOI：
10.1016/j.artint.2018.12.006
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发表时间：
2018-02
期刊：
影响因子：
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通讯作者：
Benito van der Zander;M. Liskiewicz;J. Textor
中科院分区：
文献类型：
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作者：
Benito van der Zander;M. Liskiewicz;J. Textor
从非实验数据中对因果效应的可识别性进行原则性推理是图因果模型的一个重要应用。本文重点讨论的影响，可识别的协变量调整，一种常用的估计方法。我们提出了一个算法框架，用于有效地测试，构建和枚举祖先图（AG）中的m-分隔符，这是一类图形因果模型，可以表示潜在混杂因素存在的不确定性。此外，我们证明了减少因果效应识别协变量调整汤姆分离的子图有向无环图（DAG）和最大祖先图（MAG）。联合起来，这些结果产生建设性的标准，其特征在于所有的调整集以及所有的最小和最小的调整集，用于识别所需的因果效应与多个曝光和潜在的混淆的存在下的结果。我们的研究结果扩展了这些问题的特殊情况下，现有的几个解决方案。我们的高效算法使我们能够经验性地量化协变量调整和随机DAG和MAG中的do-calculus之间的可识别性差距，涵盖了广泛的场景。我们的算法的实现在R packagedagitty中提供。
Principled reasoning about the identifiability of causal effects from non-experimental data is an important application of graphical causal models. This paper focuses on effects that are identifiable by covariate adjustment, a commonly used estimation approach. We present an algorithmic framework for efficiently testing, constructing, and enumeratingm-separators in ancestral graphs (AGs), a class of graphical causal models that can represent uncertainty about the presence of latent confounders. Furthermore, we prove a reduction from causal effect identification by covariate adjustment tom-separation in a subgraph for directed acyclic graphs (DAGs) and maximal ancestral graphs (MAGs). Jointly, these results yield constructive criteria that characterize all adjustment sets as well as all minimal and minimum adjustment sets for identification of a desired causal effect with multiple exposures and outcomes in the presence of latent confounding. Our results extend several existing solutions for special cases of these problems. Our efficient algorithms allowed us to empirically quantify the identifiability gap between covariate adjustment and the do-calculus in random DAGs and MAGs, covering a wide range of scenarios. Implementations of our algorithms are provided in the R packagedagitty.