提出首个可微分的非线性潜层因果模型发现方法，突破传统限制。
Differentiable Causal Discovery For Latent Hierarchical Causal Models
- 基于可微优化替代传统搜索，高效学习潜层因果结构
- 在高维图像数据上实现可解释的层次潜变量建模
- 适用于真实世界复杂数据，适合需要因果推理的研究者
从观测数据中发现包含潜变量的因果结构是因果发现的核心挑战。现有方法多依赖约束法和迭代离散搜索，难以扩展到大规模变量；且常假设线性或可逆性，限制了实际应用。本文提出非线性潜层因果模型的新可识别性理论，放宽了以往对潜变量确定性和外生噪声的假设。基于此，我们开发了一种新型可微分因果发现算法，能高效估计此类模型结构。据我们所知，这是首个针对非线性潜层因果模型的可微分发现方法。实验表明，该方法在准确率和可扩展性上均优于现有方法。我们在高维图像数据上学习到可解释的层次潜变量结构，并在下游任务中验证其有效性。
原文摘要 · Abstract (English)
Discovering causal structures with latent variables from observational data is a fundamental challenge in causal discovery. Existing methods often rely on constraint-based, iterative discrete searches, limiting their scalability to large numbers of variables. Moreover, these methods frequently assume linearity or invertibility, restricting their applicability to real-world scenarios. We present new theoretical results on the identifiability of nonlinear latent hierarchical causal models, relaxing previous assumptions in literature about the deterministic nature of latent variables and exogenous noise. Building on these insights, we develop a novel differentiable causal discovery algorithm that efficiently estimates the structure of such models. To the best of our knowledge, this is the first work to propose a differentiable causal discovery method for nonlinear latent hierarchical models. Our approach outperforms existing methods in both accuracy and scalability. We demonstrate its practical utility by learning interpretable hierarchical latent structures from high-dimensional image data and demonstrate its effectiveness on downstream tasks.
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