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变量对于科学发现至关重要。基于线性模型，不变预测框架建立在不变性原理之上，即目标变量在其因果父变量条件下的条件分布在多个环境或实验条件下保持不变。然而，其针对因果父变量的可识别性结果在底层图结构和生成干预数据的实验条件方面可能具有局限性。受近期提出的另一种不变性形式（称为不变匹配性质）的启发，我们在相对宽松的假设下建立了可识别性结果，从而形成了一种简单而有效的识别因果父变量的方法。我们通过多种合成数据集和真实数据集展示了所提方法的性能。