# Shadow Datasets, New challenging datasets for Causal Representation Learning

arXiv: https://arxiv.org/abs/2308.05707 · HTML: https://mlsanity.com/paper/2308.05707/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 10 Aug 2023.

## Builds on (most cited first, 14 of 14)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations](https://mlsanity.com/paper/1811.12359/) (2018)
- [DAGs with NO TEARS: Continuous Optimization for Structure Learning](https://mlsanity.com/paper/1803.01422/) (2018)
- [DAG-GNN: DAG Structure Learning with Graph Neural Networks](https://mlsanity.com/paper/1904.10098/) (2019)
- [On the Role of Sparsity and DAG Constraints for Learning Linear DAGs](https://mlsanity.com/paper/2006.10201/) (2020)
- [Learning Bayesian Networks: The Combination of Knowledge and Statistical Data](https://mlsanity.com/paper/1302.6815/) (2013)
- [Causal Discovery with Reinforcement Learning](https://mlsanity.com/paper/1906.04477/) (2019)
- [Weakly supervised causal representation learning](https://mlsanity.com/paper/2203.16437/) (2022)
- [Learning Gaussian Networks](https://mlsanity.com/paper/1302.6808/) (2013)
- [Learning Causal Semantic Representation for Out-of-Distribution Prediction](https://mlsanity.com/paper/2011.01681/) (2020)
- [A Graph Autoencoder Approach to Causal Structure Learning](https://mlsanity.com/paper/1911.07420/) (2019)
- [Dual Swap Disentangling](https://mlsanity.com/paper/1805.10583/) (2018)
- [Do-Operation Guided Causal Representation Learning with Reduced Supervision Strength](https://mlsanity.com/paper/2206.01802/) (2022)

## Built on by (most cited first, 3 of 3)

- [Counterfactual Generative Modeling with Variational Causal Inference](https://mlsanity.com/paper/2410.12730/) (2024)
- [Look, Learn and Leverage (L3): Mitigating Visual-Domain Shift and Discovering Intrinsic Relations via Symbolic Alignment](https://mlsanity.com/paper/2408.17363/) (2024)
- [Causal Representation Learning on High-Dimensional Data: Benchmarks, Reproducibility, and Evaluation Metrics](https://mlsanity.com/paper/2603.17405/) (2026)

## Abstract (arXiv)

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.