One major drawback of state-of-the-art artificial intelligence is its lack of explainability. One approach to solve the problem is taking causality into account. Causal mechanisms can be described by structural causal models. In this work, we propose a method for estimating bivariate structural causal models using a combination of normalising flows applied to density estimation and variational Gaussian process regression for post-nonlinear models. It facilitates causal discovery, i.e. distinguishing cause and effect, by either the independence of cause and residual or a likelihood ratio test. Our method which estimates post-nonlinear models can better explain a variety of real-world cause-effect pairs than a simple additive noise model. Though it remains difficult to exploit this benefit regarding all pairs from the T\"ubingen benchmark database, we demonstrate that combining the additive noise model approach with our method significantly enhances causal discovery.
翻译:最先进的人工智能的一个主要缺点是缺乏解释性。解决问题的一种方法是将因果关系考虑在内。原因机制可以用结构性因果模型来描述。在这项工作中,我们提出一种方法来估计双轨结构因果模型,将适用于密度估计的正常流动和适用于非线性后模型的可变高斯进程回归结合起来。它有利于因果发现,即通过原因和剩余或可能性比率独立测试来区分因果关系。我们估算非线性后模型的方法可以比简单的添加噪音模型更好地解释各种真实世界的因果关系。尽管仍然难以利用T\"ubingen基准数据库中所有配对的这种效益,但我们证明,将添加噪音模型方法与我们的方法结合起来,极大地促进了因果发现。