论文标题
非参数因果发现中的重复不变性
Reparametrization Invariance in non-parametric Causal Discovery
论文作者
论文摘要
因果发现估计生成观察到的数据的基本物理过程：x会导致y还是导致x？当前的方法使用结构条件将因果查询变成统计查询，仅当可用观察数据时。但是，如果这些统计查询对因果不变剂敏感怎么办？这项研究调查了这样一个不变的：X和Y之间的因果关系与X和Y的边际分布不变。我们提出了一种使用非参数估计量的算法，该算法对边际分布的变化具有牢固的变化。这样，我们可以将边缘人边缘化，并在那里内在地检查什么关系。由此产生的因果估计量具有当前方法的竞争力，并且高度重视因果查询的不确定性。一个方面与查询本身一样重要。
Causal discovery estimates the underlying physical process that generates the observed data: does X cause Y or does Y cause X? Current methodologies use structural conditions to turn the causal query into a statistical query, when only observational data is available. But what if these statistical queries are sensitive to causal invariants? This study investigates one such invariant: the causal relationship between X and Y is invariant to the marginal distributions of X and Y. We propose an algorithm that uses a non-parametric estimator that is robust to changes in the marginal distributions. This way we may marginalize the marginals, and inspect what relationship is intrinsically there. The resulting causal estimator is competitive with current methodologies and has high emphasis on the uncertainty in the causal query; an aspect just as important as the query itself.
