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

Shadow Datasets, New challenging datasets for Causal Representation Learning