A kernel-based causal learning algorithm
A kernel-based causal learning algorithm
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DOI：
10.1145/1273496.1273604
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发表时间：
2007-06
期刊：
影响因子：
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通讯作者：
Xiaohai Sun;D. Janzing;B. Scholkopf;K. Fukumizu
中科院分区：
文献类型：
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作者：
Xiaohai Sun;D. Janzing;B. Scholkopf;K. Fukumizu
我们描述了一种因果学习方法，该方法采用基于核的互协方差算子的希尔伯特-施密特范数来测量统计依赖的强度。遵循基于约束的因果学习的共同忠诚假设，我们的方法假设变量Z可能是X和Y的共同效应，如果对Z的条件作用增加了X和Y之间的依赖性。基于这一假设，我们收集“选票”的假设因果方向和定向的优势，多数原则。在大多数已知因果结构的实验中，我们的方法提供了合理的结果，优于传统的基于约束的PC算法。
We describe a causal learning method, which employs measuring the strength of statistical dependences in terms of the Hilbert-Schmidt norm of kernel-based cross-covariance operators. Following the line of the common faithfulness assumption of constraint-based causal learning, our approach assumes that a variable Z is likely to be a common effect of X and Y, if conditioning on Z increases the dependence between X and Y. Based on this assumption, we collect "votes" for hypothetical causal directions and orient the edges by the majority principle. In most experiments with known causal structures, our method provided plausible results and outperformed the conventional constraint-based PC algorithm.