提出新方法在噪声相关时仍能准确发现因果关系
Causal discovery under mean independence and linearity
- 用单向均值独立代替完全独立假设，更符合实际
- 在模拟中噪声相关时仍优于传统LiNGAM方法
- 适合处理真实世界中存在共同影响的因果推断
传统因果发现方法如LiNGAM依赖于扰动项相互独立的假设，但该假设在共享波动、共同尺度效应等情况下极易失效。本文提出线性均值独立无环模型（LiMIAM），将全独立假设放宽为单边均值独立约束。在有限阶条件下，源节点可泛识别，从而可递归恢复兼容的因果顺序。证明过程具有构造性，导出直接基于残差的算法DirectLiMIAM。在均值独立但相关的扰动下，模拟结果显示DirectLiMIAM显著优于LiNGAM。大规模原油市场实证分析表明，独立性假设不成立，而DirectLiMIAM成功恢复了从政策到生产、再到价格与通胀的合理因果链。
原文摘要 · Abstract (English)
Causal discovery methods such as LiNGAM identify causal structure from observational data by assuming mutually independent disturbances. This assumption is fragile: shared volatility, common scale effects, or other forms of dependence can cause the methods to recover the wrong causal order, even with infinite data. We introduce the Linear Mean-Independent Acyclic Model (LiMIAM), which replaces full independence with weaker one-sided mean-independence restrictions on the disturbances. Under finite-order consequences of these restrictions, source nodes are generically identifiable, and hence a compatible causal order can be recovered recursively. Our proof is constructive and leads to DirectLiMIAM, a sequential residual-based algorithm for causal discovery under dependent noise. In simulations with mean-independent but dependent disturbances, DirectLiMIAM outperforms LiNGAM methods. A large-scale empirical application to the oil market highlights the implausibility of the independence assumption and the ability of DirectLiMIAM to recover a realistic causal ordering, from policy to production and from prices to inflation.
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