This paper proposes a Disentangled gEnerative cAusal Representation (DEAR)
learning method under appropriate supervised information. 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 related. We show that previous methods with independent priors fail to
disentangle causally related factors even under supervision. 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 distribution for a bidirectional generative model. The prior is then
trained jointly with a generator and an encoder using a suitable GAN algorithm
incorporated with supervised information on the ground-truth factors and their
underlying causal structure. We provide theoretical justification on the
identifiability and asymptotic convergence of the proposed method. We conduct
extensive experiments on both synthesized and real data sets 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。