Learning predictors that do not rely on spurious correlations involves building causal representations. However, learning such a representation is very challenging. We, therefore, formulate the problem of learning a causal representation from high dimensional data and study causal recovery with synthetic data. This work introduces a latent variable decoder model, Decoder BCD, for Bayesian causal discovery and performs experiments in mildly supervised and unsupervised settings. We present a series of synthetic experiments to characterize important factors for causal discovery and show that using known intervention targets as labels helps in unsupervised Bayesian inference over structure and parameters of linear Gaussian additive noise latent structural causal models.

翻译：不依赖虚假关联的学习预测器涉及建立因果关系。然而,学习这种代表性非常具有挑战性。因此,我们从高维数据中找出因果代表,并研究合成数据的因果恢复。这项工作引入了一种潜在的变数解码模型Decoder BCD,用于贝叶斯因果发现,并在温和、不受监督的环境中进行实验。我们提出了一系列合成实验,以辨别因果发现的重要因素,并表明使用已知的干预目标作为标签有助于未经监督的贝叶斯对直线高斯添加噪音潜在结构性因果模型的结构和参数的推断。