迈向使用基于置换的检验来证伪因果图
Toward Falsifying Causal Graphs Using a Permutation-Based Test
AI总结：
提出基于节点置换构建基线的因果图一致性检验方法，通过比较不一致数量判断图是否显著优于随机，实验验证其能保留真实图并证伪错误图。
摘要：
理解系统中变量间的因果关系对于解释和控制其行为至关重要。然而，对于许多真实世界的系统，真实的因果图并不容易获得，人们必须依赖算法或领域专家做出的预测。因此，在使用因果图进行下游任务之前，定量评估其好坏的指标提供了有用的检查。现有指标提供图与观测数据之间不一致的绝对数量，而在没有基线的情况下，从业者面临着多少不一致是可以接受或预期的难题。在此，我们提出了一种新的一致性指标，通过节点置换构建基线。通过将不一致的数量与基线上的进行比较，我们推导出一个可解释的指标，捕捉图是否显著优于随机。在来自不同领域（包括生物学和云监控）的模拟和真实数据集上进行评估，我们证明真实图不会被我们的指标证伪，而假设用户给出的错误图则很可能被证伪。
英文摘要：
Understanding causal relationships among the variables of a system is paramount to explain and control its behavior. For many real-world systems, however, the true causal graph is not readily available and one must resort to predictions made by algorithms or domain experts. Therefore, metrics that quantitatively assess the goodness of a causal graph provide helpful checks before using it in downstream tasks. Existing metrics provide an $\textit{absolute}$ number of inconsistencies between the graph and the observed data, and without a baseline, practitioners are left to answer the hard question of how many such inconsistencies are acceptable or expected. Here, we propose a novel consistency metric by constructing a baseline through node permutations. By comparing the number of inconsistencies with those on the baseline, we derive an interpretable metric that captures whether the graph is significantly better than random. Evaluating on both simulated and real data sets from various domains, including biology and cloud monitoring, we demonstrate that the true graph is not falsified by our metric, whereas the wrong graphs given by a hypothetical user are likely to be falsified.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
