论文标题
贝叶斯因果发现的潜在变量模型
Latent Variable Models for Bayesian Causal Discovery
论文作者
论文摘要
不依赖虚假相关性的学习预测因素涉及建立因果关系。但是，学习这样的表示非常具有挑战性。因此，我们制定了从高维数据中学习因果表示的问题，并通过合成数据研究因果恢复。这项工作引入了贝叶斯因果发现的潜在变量解码器模型BCD，并在轻度监督和无监督的设置中进行了实验。我们提出了一系列的合成实验，以表征因果发现的重要因素，并表明将已知的干预目标用作标签有助于无监督的贝叶斯推断对线性高斯添加噪声潜在结构性因果模型的结构和参数。
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.
