无需真实因果结构，即可筛选出更简洁且符合统计条件的因果图模型。
Choosing DAG Models Using Markov and Minimal Edge Count in the Absence of Ground Truth
- 提出马尔可夫检验法，判断因果图是否满足马尔可夫条件。
- 通过交叉算法精简搜索，剔除不满足马尔可夫性或非边最小的模型。
- 适用于无真值场景，适合调试因果发现算法与参数调优。
我们提出一种新颖的非参数点一致性统计检验方法（马尔可夫检验器），用于在给定数据集的情况下，对有向无环图（DAG）或完整部分有向无环图（CPDAG）模型检验其马尔可夫条件。同时引入交叉算法精简搜索（CAFS）方法，用于排除不通过马尔可夫检验或非边最小的DAG模型。边最小性此前由Raskutti和Uhler作为非参数简洁性标准使用，但CAFS可轻松推广至其他简洁性准则。该方法无需依赖真实因果结构，因此适用于从数据中寻找近似正确的因果结构学习算法及参数设置。我们还提供一个软件工具，适用于较大或密集模型，前提是存在快速的点一致性条件独立检验。模拟实验表明，即使没有真实结构参考，CAFS仍能选出近似正确的模型。
原文摘要 · Abstract (English)
We give a novel nonparametric pointwise consistent statistical test (the Markov Checker) of the Markov condition for directed acyclic graph (DAG) or completed partially directed acyclic graph (CPDAG) models given a dataset. We also introduce the Cross-Algorithm Frugality Search (CAFS) for rejecting DAG models that either do not pass the Markov Checker test or that are not edge minimal. Edge minimality has been used previously by Raskutti and Uhler as a nonparametric simplicity criterion, though CAFS readily generalizes to other simplicity conditions. Reference to the ground truth is not necessary for CAFS, so it is useful for finding causal structure learning algorithms and tuning parameter settings that output causal models that are approximately true from a given data set. We provide a software tool for this analysis that is suitable for even quite large or dense models, provided a suitably fast pointwise consistent test of conditional independence is available. In addition, we show in simulation that the CAFS procedure can pick approximately correct models without knowing the ground truth.
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