论文标题
结构性因果模型的批判视图
A Critical View of the Structural Causal Model
论文作者
论文摘要
在单变量的情况下，我们表明，通过比较单变量因果的个体复杂性，可以识别原因和效果，而无需考虑它们的相互作用。在我们的框架中，复杂性是由在分布的分位数上操作的自动编码器的重建误差来捕获的。比较两个自动编码器的重建误差，一个用于每个变量，可在公认的因果方向基准上表现出色。因此，关于两者中哪个是原因的决定，哪些是基于因果关系，而是基于复杂性。 在多元案例中，人们可以确保因果关系的复杂性是平衡的，我们提出了一种新的对抗训练方法，该方法模仿了因果模型的分离结构。我们证明，在多维情况下，这种建模可能仅适合数据符合因果关系。此外，唯一性结果表明，学到的模型能够识别潜在的因果关系和残留（噪声）成分。我们的多维方法优于合成和现实世界数据集的文献方法。
In the univariate case, we show that by comparing the individual complexities of univariate cause and effect, one can identify the cause and the effect, without considering their interaction at all. In our framework, complexities are captured by the reconstruction error of an autoencoder that operates on the quantiles of the distribution. Comparing the reconstruction errors of the two autoencoders, one for each variable, is shown to perform surprisingly well on the accepted causality directionality benchmarks. Hence, the decision as to which of the two is the cause and which is the effect may not be based on causality but on complexity. In the multivariate case, where one can ensure that the complexities of the cause and effect are balanced, we propose a new adversarial training method that mimics the disentangled structure of the causal model. We prove that in the multidimensional case, such modeling is likely to fit the data only in the direction of causality. Furthermore, a uniqueness result shows that the learned model is able to identify the underlying causal and residual (noise) components. Our multidimensional method outperforms the literature methods on both synthetic and real world datasets.
