Discovering causal relations among semantic factors is an emergent topic in
representation learning. Most causal representation learning (CRL) methods are
fully supervised, which is impractical due to costly labeling. To resolve this
restriction, weakly supervised CRL methods were introduced. To evaluate CRL
performance, four existing datasets, Pendulum, Flow, CelebA(BEARD) and
CelebA(SMILE), are utilized. However, existing CRL datasets are limited to
simple graphs with few generative factors. Thus we propose two new datasets
with a larger number of diverse generative factors and more sophisticated
causal graphs. In addition, current real datasets, CelebA(BEARD) and
CelebA(SMILE), the originally proposed causal graphs are not aligned with the
dataset distributions. Thus, we propose modifications to them.

发现语义因素之间的因果关系是表示学习中的一个新兴话题。而多数因果表示学习（CRL）方法都是完全有监督的，但由于标注成本高昂，因此并不实用。为了克服这一限制，引入了弱监督的 CRL 方法。为了评估 CRL 性能，利用了四个现有数据集，即 Pendulum、Flow、CelebA (BEARD) 和 CelebA (SMILE)。然而，现有的 CRL 数据集只涉及到简单的图形和少量生成因素。因此，我们提出了两个全新的数据集，包含更多多样化的生成因素和更复杂的因果图。此外，当前的真实数据集 CelebA (BEARD) 和 CelebA (SMILE) 的原始因果图与数据集分布不一致，因此我们对它们进行了修改。