Structural restrictions in local causal discovery: identifying direct causes of a target variable
研究了仅从观测数据中识别一个目标变量的直接原因集，不依赖干预或完整因果图，提出了两种实用算法并在基准和真实数据上验证了效果。
Summary We consider the problem of learning a set of direct causes of a target variable from an observational joint distribution. Learning directed acyclic graphs that represent the causal structure is a fundamental problem in science. Several results are known when the full directed acyclic graph is identifiable from the distribution, such as when a nonlinear Gaussian data-generating process is assumed. Here, we are interested only in identifying the direct causes of one target variable (local causal structure), not the full directed acyclic graph. This allows us to relax the identifiability assumptions and develop possibly faster and more robust algorithms. In contrast to the invariance causal prediction framework, here the only assumption is that we observe one environment without any interventions. We discuss different assumptions for the data-generating process of the target variable under which the set of direct causes is identifiable from the distribution. While doing so, we impose essentially no assumptions on the variables other than the target variable. In addition to the novel identifiability results, we provide two practical algorithms for estimating the direct causes from a finite random sample and demonstrate their effectiveness on several benchmark and real datasets.