Causal Inference by Direction of Information
Causal Inference by Direction of Information
复制标题
通过信息方向进行因果推断
DOI：
10.1137/1.9781611974010.102
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Jilles Vreeken
中科院分区：
文献类型：
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作者：
Jilles Vreeken
我们关注的是数据驱动的因果推理。特别地，我们提出了一种基于算法信息论的因果推理的新原理，即Kolmogorov复杂性。简而言之，我们确定一个数据对象提供了多少关于另一个数据对象的信息，反之亦然，并通过信息的最强方向确定最可能的因果方向。为了将这一原则应用到实践中，我们提出了ERGO，这是一种用于推断多元实值数据对之间因果方向的有效实例化方法。ERGO是基于累积性和香农性的。因此，我们不必假定分布，也不必限制相关性的类型。对合成数据、基准数据和真实世界数据的广泛经验评估表明，ERGO对噪声和维度都具有健壮性，效率高，并且远远超过最先进的水平。
We focus on data-driven causal inference. In particular, we propose a new principle for causal inference based on algorithmic information theory, i.e. Kolmogorov complexity. In a nutshell, we determine how much information one data object gives about the other, and vice versa, and identify the most likely causal direction by the strongest direction of information. To apply this principle in practice, we propose ERGO, an efficient instantiation for inferring the causal direction between multivariate real-valued data pairs. ERGO is based on cumulative and Shannon entropy. Therewith, we do not have to assume distributions, nor have to restrict the type of correlation. Extensive empirical evaluation on synthetic, benchmark, and real-world data shows that ERGO is robust against both noise and dimensionality, efficient, and outperforms the state of the art by a wide margin.