Identifying the direct causes or causal parents of a target variable is crucial for scientific discovery. Focusing on linear models, the invariant prediction framework was built upon the invariance principle, namely, the conditional distribution of the target variable given its causal parents is invariant across multiple environments or experimental conditions. However, their identifiability results for causal parents can be restrictive with respect to the underlying graph structure and the experimental conditions for generating interventional data. Motivated by a recent alternative formulation of invariance, called the invariant matching property, we establish identifiability results under relatively mild assumptions, which leads to a simple yet effective procedure for identifying causal parents. We demonstrate the performance of the proposed method over various synthetic and real datasets.

翻译：识别目标变量的直接原因或因果父节点对科学发现至关重要。针对线性模型，不变性预测框架基于不变性原理构建，即目标变量在其因果父节点下的条件分布在多个环境或实验条件下保持不变。然而，其因果父节点的可识别性结果受限于底层图结构及生成干预数据的实验条件。受近期提出的另一种不变性公式——不变匹配性质的启发，我们在相对宽松的假设下建立了可识别性结果，从而提出了一种简单而有效的因果父节点识别方法。我们通过在多种合成数据集和真实数据集上验证了所提方法的性能。