This paper proposes a Disentangled gEnerative cAusal Representation (DEAR) learning method. Unlike existing disentanglement methods that enforce independence of the latent variables, we consider the general case where the underlying factors of interests can be causally correlated. We show that previous methods with independent priors fail to disentangle causally correlated factors. Motivated by this finding, we propose a new disentangled learning method called DEAR that enables causal controllable generation and causal representation learning. The key ingredient of this new formulation is to use a structural causal model (SCM) as the prior for a bidirectional generative model. The prior is then trained jointly with a generator and an encoder using a suitable GAN loss. Theoretical justification on the proposed formulation is provided, which guarantees disentangled causal representation learning under appropriate conditions. We conduct extensive experiments on both synthesized and real datasets to demonstrate the effectiveness of DEAR in causal controllable generation, and the benefits of the learned representations for downstream tasks in terms of sample efficiency and distributional robustness.

本文提出一种名为DEAR的去混合生成因果表征学习方法，通过使用结构性因果模型作为先验分布和适当的GAN算法，利用监督信息对因果结构进行学习，从而实现因果可控生成并提高下游任务的样本效率。

弱监督下解缠绕的生成因果表征学习