论文标题
得分匹配使非线性加性噪声模型的因果发现
Score matching enables causal discovery of nonlinear additive noise models
论文作者
论文摘要
本文演示了如何从非线性添加剂（高斯）噪声模型中数据分布的得分中恢复因果图。使用得分匹配算法作为构建块，我们展示了如何设计新一代可扩展的因果发现方法。为了展示我们的方法，我们还提出了一种新的有效方法来近似分数的雅各布式，从而能够恢复因果图。从经验上讲，我们发现称为得分的新算法与最先进的因果发现方法具有竞争力，同时又要快得多。
This paper demonstrates how to recover causal graphs from the score of the data distribution in non-linear additive (Gaussian) noise models. Using score matching algorithms as a building block, we show how to design a new generation of scalable causal discovery methods. To showcase our approach, we also propose a new efficient method for approximating the score's Jacobian, enabling to recover the causal graph. Empirically, we find that the new algorithm, called SCORE, is competitive with state-of-the-art causal discovery methods while being significantly faster.
