# Causal Flow-based Variational Auto-Encoder for Disentangled Causal Representation Learning

arXiv: https://arxiv.org/abs/2304.09010 · HTML: https://mlsanity.com/paper/2304.09010/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2023.

## Built on by (most cited first, 4 of 4)

- [Causal normalizing flows: from theory to practice](https://mlsanity.com/paper/2306.05415/) (2023)
- [Counterfactual Generation with Identifiability Guarantees](https://mlsanity.com/paper/2402.15309/) (2024)
- [FlexCausal: Flexible Causal Disentanglement via Structural Flow Priors and Manifold-Aware Interventions](https://mlsanity.com/paper/2601.21567/) (2026)
- [C-kNN-LSH: A Nearest-Neighbor Algorithm for Sequential Counterfactual Inference](https://mlsanity.com/paper/2602.02371/) (2026)

## Abstract (arXiv)

Disentangled representation learning aims to learn low-dimensional representations where each dimension corresponds to an underlying generative factor. While the Variational Auto-Encoder (VAE) is widely used for this purpose, most existing methods assume independence among factors, a simplification that does not hold in many real-world scenarios where factors are often interdependent and exhibit causal relationships. To overcome this limitation, we propose the Disentangled Causal Variational Auto-Encoder (DCVAE), a novel supervised VAE framework that integrates causal flows into the representation learning process, enabling the learning of more meaningful and interpretable disentangled representations. We evaluate DCVAE on both synthetic and real-world datasets, demonstrating its superior ability in causal disentanglement and intervention experiments. Furthermore, DCVAE outperforms state-of-the-art methods in various downstream tasks, highlighting its potential for learning true causal structures among factors.