Standard diffusion models are flexible estimators of complex distributions, but they do not encode causal structures and therefore do not by themselves support causal analysis. We propose a causality-encoded diffusion framework that incorporates a known directed acyclic graph by training conditional diffusion models consistent with the graph factorisation. The resulting sampler approximately recovers the observational distribution and enables interventional sampling by fixing intervened variables while propagating effects through the graph during reverse diffusion. Building on this interventional simulator, we develop a resampling-based test for directed edges that generates null replicates under a candidate graph. We establish convergence guarantees for observational and interventional distribution estimation, with rates governed by the maximum local dimension rather than the ambient dimension, and prove asymptotic control of type I error for the edge test. Simulations show improved interventional distribution recovery relative to baselines, with near-nominal size and favourable power in inference. An application to flow cytometry data demonstrates practical utility of the proposed method in assessing disputed signalling linkages.

翻译：标准扩散模型是复杂分布的灵活估计器，但未编码因果结构，因此本身不支持因果分析。我们提出了一种因果编码扩散框架，通过训练与有向无环图分解一致的因果条件扩散模型，将已知有向无环图融入其中。由此得到的采样器可近似恢复观测分布，并能在反向扩散过程中通过固定干预变量并沿因果图传播效应，实现干预采样。基于这一干预模拟器，我们开发了一种基于重采样的有向边检验方法，能在候选图假设下生成零复制。我们建立了观测分布与干预分布估计的收敛保证，其收敛速率由最大局部维度而非整体维度决定，并证明了边检验中一类错误概率的渐近可控性。仿真实验表明，相比基线方法，本方法在干预分布恢复上表现更优，且具有接近名义水平的检验尺寸和良好的检验功效。对流式细胞术数据的应用展示了所提方法在评估存疑信号通路方面的实用价值。