论香农能力与因果估计

Rahul Kidambi, Sreeram Kannan
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引用次数: 2

摘要

本文研究了从纯观测数据估计因果关系的问题。我们从一对随机变量(X,Y)中观察样本,并希望估计是X导致Y还是Y导致X。任何联合分布都可以被分解为pX,Y = pX pY|X = pY pX|Y,因此,如果没有进一步的假设,就不能从联合分布中推断出“因果”方向。在本文中,我们提出并研究了香农容量作为因果方向性估计度量的效用。这为未来的研究开辟了几个开放的问题和方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On Shannon capacity and causal estimation
The problem of estimating causal relationships from purely observational data is studied in this paper. We observe samples from a pair of random variables (X,Y) and wish to estimate whether X causes Y or Y causes X. Any joint distribution can be factored as pX,Y = pX pY|X = pY pX|Y and therefore the “causal” direction cannot be inferred from the joint distribution without further assumptions. In this paper, we propose and study the utility of Shannon capacity as a metric for causal directionality estimation. This opens up several open questions and directions for future study.
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