通过因果关系分布不变性，高效推断因果图。
Causal Graph Learning via Distributional Invariance of Cause-Effect Relationship
- 利用条件分布对先验变化的稳定性检验因果关系
- 在多数据子集上验证，实现二次复杂度算法
- 比现有方法快25倍，适合大规模数据
本文提出一种新框架，从观测数据中恢复因果图。核心思想是：给定原因时，结果的条件分布不受原因先验分布变化的影响。基于这一不变性，可通过检查不同下采样子集上效应-原因条件分布的方差来直接测试潜在因果关系。这些子集反映不同的原因先验，同时保持因果条件关系不变。结合该不变性检验与大多数因果图具有稀疏性的经验观察，我们设计了一种算法，其时间复杂度为变量数的二次方，相比现有最优方法处理速度提升高达25倍。在多个大规模数据集基准上的实验证明，本方法性能优于或相当，且具备更强可扩展性。
原文摘要 · Abstract (English)
This paper introduces a new framework for recovering causal graphs from observational data, leveraging the observation that the distribution of an effect, conditioned on its causes, remains invariant to changes in the prior distribution of those causes. This insight enables a direct test for potential causal relationships by checking the variance of their corresponding effect-cause conditional distributions across multiple downsampled subsets of the data. These subsets are selected to reflect different prior cause distributions, while preserving the effect-cause conditional relationships. Using this invariance test and exploiting an (empirical) sparsity of most causal graphs, we develop an algorithm that efficiently uncovers causal relationships with quadratic complexity in the number of observational variables, reducing the processing time by up to 25x compared to state-of-the-art methods. Our empirical experiments on a varied benchmark of large-scale datasets show superior or equivalent performance compared to existing works, while achieving enhanced scalability.
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