Research map / Probabilistic and scientific modeling / Causal discovery
CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add causal structure to the supervised VAE | CausalVAE vs ConditionVAE · Pendulum | MIC: 93.8 ±3.3 -> 95.1 ±2.4 | Helped |
| Add causal structure to the supervised VAE | CausalVAE vs ConditionVAE · Pendulum | TIC: 80.5 ±1.4 -> 81.6 ±1.9 | Helped |
| Add causal structure to the supervised VAE | CausalVAE vs ConditionVAE · Flow | MIC: 75.5 ±2.3 -> 72.1 ±1.3 | Hurt |
| Add causal structure to the supervised VAE | CausalVAE vs ConditionVAE · Flow | TIC: 56.5 ±1.8 -> 56.4 ±1.6 | No clear effect |
| Add causal structure to the supervised VAE | CausalVAE vs ConditionVAE · CelebA(SMILE) | MIC: 78.8 ±10.9 -> 83.7 ±6.2 | Helped |
| Add causal structure to the supervised VAE | CausalVAE vs ConditionVAE · CelebA(SMILE) | TIC: 66.1 ±12.1 -> 71.6 ±7.2 | Helped |
| Add causal structure to the supervised VAE | CausalVAE vs ConditionVAE · CelebA(BEARD) | MIC: 89.8 ±6.2 -> 92.3 ±5.6 | Helped |
| Add causal structure to the supervised VAE | CausalVAE vs ConditionVAE · CelebA(BEARD) | TIC: 78.7 ±7.7 -> 83.3 ±8.6 | Helped |
| Add the Mask Layer and conditional prior p(z|u) (ablation) | CausalVAE vs CausalVAE-unsup · Pendulum | MIC: 21.2 ±1.4 -> 95.1 ±2.4 | Helped |
| Add the Mask Layer and conditional prior p(z|u) (ablation) | CausalVAE vs CausalVAE-unsup · Flow | MIC: 22.8 ±2.7 -> 72.1 ±1.3 | Helped |
| Add the Mask Layer and conditional prior p(z|u) (ablation) | CausalVAE vs CausalVAE-unsup · CelebA(SMILE) | MIC: 27.2 ±5.3 -> 83.7 ±6.2 | Helped |
| Add the Mask Layer and conditional prior p(z|u) (ablation) | CausalVAE vs CausalVAE-unsup · CelebA(BEARD) | MIC: 11.4 ±1.5 -> 92.3 ±5.6 | Helped |
Closest papers by meaning
- Disentangled Representation Learning in Non-Markovian Causal Systems (NeurIPS 2024 · similarity 0.91)
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- Identifiability Guarantees for Causal Disentanglement from Purely Observational Data (NeurIPS 2024 · similarity 0.88)
- Graph-based Unsupervised Disentangled Representation Learning via Multimodal Large Language Models (NeurIPS 2024 · similarity 0.88)
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