论文标题
线性结构方程模型中的祖先回归
Ancestor regression in linear structural equation models
论文作者
论文摘要
我们为线性结构方程模型中的因果发现提供了一种新方法。我们根据线性模型中的统计测试提出了一个简单的``技巧''，该模型可以区分任何给定变量的祖先和非官方。自然，这可以扩展到所有变量之间的因果秩序。我们至少渐近地为假因果发现提供明确的错误控制。即使在高斯性下，由于无法识别的结构失败，这也是如此。这些类型I错误保证是以减少经验能力为代价的。此外，我们提供了渐近p值的渐近有效优点，以评估多元数据是否来自线性结构方程模型。
We present a new method for causal discovery in linear structural equation models. We propose a simple ``trick'' based on statistical testing in linear models that can distinguish between ancestors and non-ancestors of any given variable. Naturally, this can then be extended to estimating the causal order among all variables. We provide explicit error control for false causal discovery, at least asymptotically. This holds true even under Gaussianity, where other methods fail due to non-identifiable structures. These type I error guarantees come at the cost of reduced empirical power. Additionally, we provide an asymptotically valid goodness of fit p-value to assess whether multivariate data stems from a linear structural equation model.
