论文标题
一致的混淆力量估计器
A Consistent Estimator for Confounding Strength
论文作者
论文摘要
观察数据上的回归可能无法在存在未观察到的混杂的情况下捕获因果关系。混淆力量衡量这种不匹配，但估计它需要自己的其他假设。一个常见的假设是因果机制的独立性，它依赖于高维度中的浓度现象。尽管高维度可以估算混杂的强度，但它们也需要适应的估计器。在本文中，我们通过Janzing和Schölkopf（2018）得出了混杂强度估计器的渐近行为，并表明它通常不一致。然后，我们使用随机矩阵理论的工具来得出一个适应性的一致估计器。
Regression on observational data can fail to capture a causal relationship in the presence of unobserved confounding. Confounding strength measures this mismatch, but estimating it requires itself additional assumptions. A common assumption is the independence of causal mechanisms, which relies on concentration phenomena in high dimensions. While high dimensions enable the estimation of confounding strength, they also necessitate adapted estimators. In this paper, we derive the asymptotic behavior of the confounding strength estimator by Janzing and Schölkopf (2018) and show that it is generally not consistent. We then use tools from random matrix theory to derive an adapted, consistent estimator.
