Causal learning has long concerned itself with the accurate recovery of underlying causal mechanisms. Such causal modelling enables better explanations of out-of-distribution data. Prior works on causal learning assume that the high-level causal variables are given. However, in machine learning tasks, one often operates on low-level data like image pixels or high-dimensional vectors. In such settings, the entire Structural Causal Model (SCM) -- structure, parameters, \textit{and} high-level causal variables -- is unobserved and needs to be learnt from low-level data. We treat this problem as Bayesian inference of the latent SCM, given low-level data. For linear Gaussian additive noise SCMs, we present a tractable approximate inference method which performs joint inference over the causal variables, structure and parameters of the latent SCM from random, known interventions. Experiments are performed on synthetic datasets and a causally generated image dataset to demonstrate the efficacy of our approach. We also perform image generation from unseen interventions, thereby verifying out of distribution generalization for the proposed causal model.

翻译：因果关系学长期以来一直关注内在因果机制的准确恢复。这种因果建模有助于更好地解释分配外的数据。先前的因果学习工作假定提供了高因果变数。然而,在机器学习任务中,常常使用低层次数据,如图像像素或高维矢量。在这种环境下,整个结构性因果模型(SCM) -- -- 结构、参数、参数、\textit{和}高层次因果变数 -- -- 是无法观察到的,需要从低层次数据中学习。我们将此问题作为潜在SCM的贝耶斯推论处理,提供低层次数据。对于线性高斯添加性噪音SCM,我们提出了一个可移植的近似推论方法,对随机已知干预产生的潜在SCM的因果变数、结构和参数进行联合推断。对合成数据集和因果生成的图像数据集进行了实验,以证明我们的方法的有效性。我们还从无形的干预中进行图像生成,从而核实拟议的因果模型的分布的全局性。