论文标题
因果关系距离
A Ladder of Causal Distances
论文作者
论文摘要
因果发现是从数据自动构建因果模型的任务，在整个科学中具有重要意义。理想情况下，评估因果发现算法的性能应涉及将推论模型与可用于基准数据集的地面模型进行比较，这反过来又需要在因果模型之间有距离的概念。尽管以前提出过这种距离，但它们通过专注于比较因果模型的图形性能而受到限制。在这里，我们通过定义从模型引起的因果分布而不是仅从其图形结构中得出的距离来克服这一限制。 Pearl and MacKenzie（2018）在一个称为“因果关系阶梯”的层次结构中安排了因果模型的性质，跨越了三个梯级：观察性，介入，介入和反事实。在这个组织之后，我们介绍了三个距离的层次结构，每个梯子的每个梯子都有一个。我们的定义具有直觉上的吸引力，并且有效地计算了大约。我们将因果关系距离通过基准标准因果发现系统在合成和现实世界中的数据集上使用，以便为基础真相模型提供。最后，我们通过简要讨论因果发现技术的评估，突出了因果距离的有用性。
Causal discovery, the task of automatically constructing a causal model from data, is of major significance across the sciences. Evaluating the performance of causal discovery algorithms should ideally involve comparing the inferred models to ground-truth models available for benchmark datasets, which in turn requires a notion of distance between causal models. While such distances have been proposed previously, they are limited by focusing on graphical properties of the causal models being compared. Here, we overcome this limitation by defining distances derived from the causal distributions induced by the models, rather than exclusively from their graphical structure. Pearl and Mackenzie (2018) have arranged the properties of causal models in a hierarchy called the "ladder of causation" spanning three rungs: observational, interventional, and counterfactual. Following this organization, we introduce a hierarchy of three distances, one for each rung of the ladder. Our definitions are intuitively appealing as well as efficient to compute approximately. We put our causal distances to use by benchmarking standard causal discovery systems on both synthetic and real-world datasets for which ground-truth causal models are available. Finally, we highlight the usefulness of our causal distances by briefly discussing further applications beyond the evaluation of causal discovery techniques.
