提出可学习任意离散依赖关系的可微结构学习框架，突破传统假设限制。
Differentiable Structure Learning and Causal Discovery for General Binary Data
- 构建通用离散变量间复杂依赖的可微学习机制
- 在弱假设下实现结构至马尔可夫等价类的可识别性
- 适合处理非线性、高阶交互的离散数据建模任务
现有离散数据的可微结构学习方法通常假设数据来自特定结构方程模型，但这些假设可能与真实生成过程不符，限制了方法的普适性。此外，当前方法常忽略离散数据中的复杂依赖结构，仅考虑线性效应。本文提出一种能捕捉离散变量间任意依赖关系的可微结构学习框架。我们证明，尽管一般离散模型仅从观测数据无法唯一确定，但可以完整刻画所有相容的参数与结构集合。此外，在温和假设下，可实现至马尔可夫等价类的可识别性。我们将学习问题统一为一个可微优化任务，避免了以往方法中不切实际的简化。实验表明，该方法能有效捕捉离散数据中的复杂关系。
原文摘要 · Abstract (English)
Existing methods for differentiable structure learning in discrete data typically assume that the data are generated from specific structural equation models. However, these assumptions may not align with the true data-generating process, which limits the general applicability of such methods. Furthermore, current approaches often ignore the complex dependence structure inherent in discrete data and consider only linear effects. We propose a differentiable structure learning framework that is capable of capturing arbitrary dependencies among discrete variables. We show that although general discrete models are unidentifiable from purely observational data, it is possible to characterize the complete set of compatible parameters and structures. Additionally, we establish identifiability up to Markov equivalence under mild assumptions. We formulate the learning problem as a single differentiable optimization task in the most general form, thereby avoiding the unrealistic simplifications adopted by previous methods. Empirical results demonstrate that our approach effectively captures complex relationships in discrete data.
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