提出灵活因果解耦框架，更好建模真实世界复杂因果关系。
FlexCausal: Flexible Causal Disentanglement via Structural Flow Priors and Manifold-Aware Interventions
- 用分块对角协方差变分自编码器替代传统对角近似，保留潜在维度相关性。
- 在合成与真实数据集上，因果结构恢复准确率显著优于现有方法。
- 适合需要高保真生成与可解释因果推理的研究者使用。
因果解耦表示学习（CDRL）旨在从观测中学习并解耦低维表示及其潜在因果结构。然而，现有方法依赖于标准均值场近似和对角后验协方差，导致所有潜变量维度被强制去相关。同时，这些方法通常假设外生噪声服从各向同性高斯先验，难以捕捉真实因果因子中普遍存在的复杂非高斯统计特性。为此，我们提出 FlexCausal，一种基于分块对角协方差的变分自编码器框架。该框架采用因子化流先验，更真实地建模外生噪声的复杂分布，有效解耦因果机制与分布统计。通过结合监督对齐目标与反事实一致性约束，确保学习的潜空间与真实因果关系精确对应。最后引入流形感知的相对干预策略，保障生成质量。在合成与真实数据集上的实验表明，FlexCausal 显著优于其他方法。
原文摘要 · Abstract (English)
Causal Disentangled Representation Learning(CDRL) aims to learn and disentangle low dimensional representations and their underlying causal structure from observations. However, existing disentanglement methods rely on a standard mean-field approximation with a diagonal posterior covariance, which decorrelates all latent dimensions. Additionally, these methods often assume isotropic Gaussian priors for exogenous noise, failing to capture the complex, non-Gaussian statistical properties prevalent in real-world causal factors. Therefore, we propose FlexCausal, a novel CDRL framework based on a block-diagonal covariance VAE. FlexCausal utilizes a Factorized Flow-based Prior to realistically model the complex densities of exogenous noise, effectively decoupling the learning of causal mechanisms from distributional statistics. By integrating supervised alignment objectives with counterfactual consistency constraints, our framework ensures a precise structural correspondence between the learned latent subspaces and the ground-truth causal relations. Finally, we introduce a manifold-aware relative intervention strategy to ensure high-fidelity generation. Experimental results on both synthetic and real-world datasets demonstrate that FlexCausal significantly outperforms other methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。