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.

该研究探讨了从高维数据中学习因果表示的挑战性问题，并引入了一种基于潜变量解码器模型的贝叶斯因果发现方法，在半监督和无监督的情况下进行了实验，表明使用已知的干预目标能够帮助结构和参数的无监督贝叶斯推断。