Under stringent model type and variable distribution assumptions, differentiable score-based causal discovery methods learn a directed acyclic graph (DAG) from observational data by evaluating candidate graphs over an average score function. Despite great success in low-dimensional linear systems, it has been observed that these approaches overly exploit easier-to-fit samples, thus inevitably learning spurious edges. Worse still, inherent mostly in these methods the common homogeneity assumption can be easily violated, due to the widespread existence of heterogeneous data in the real world, resulting in performance vulnerability when noise distributions vary. We propose a simple yet effective model-agnostic framework to boost causal discovery performance by dynamically learning the adaptive weights for the Reweighted Score function, ReScore for short, where the weights tailor quantitatively to the importance degree of each sample. Intuitively, we leverage the bilevel optimization scheme to \wx{alternately train a standard DAG learner and reweight samples -- that is, upweight the samples the learner fails to fit and downweight the samples that the learner easily extracts the spurious information from. Extensive experiments on both synthetic and real-world datasets are carried out to validate the effectiveness of ReScore. We observe consistent and significant boosts in structure learning performance. Furthermore, we visualize that ReScore concurrently mitigates the influence of spurious edges and generalizes to heterogeneous data. Finally, we perform the theoretical analysis to guarantee the structure identifiability and the weight adaptive properties of ReScore in linear systems. Our codes are available at https://github.com/anzhang314/ReScore.
翻译:在严格的模型类型和可变分布假设下,基于分分数的不同因果发现方法通过对平均分数函数的候选图表进行评估,从观测数据中学习了定向循环图(DAG)。尽管在低维线性系统中取得了巨大成功,但人们观察到,这些方法过度利用较容易使用的样本,从而不可避免地发现假边缘。更糟糕的是,由于在现实世界中广泛存在差异性数据,共同同质性假设很容易被违反,从而导致在噪音分布变化时出现性能脆弱性。我们提议了一个简单而有效的模型-不可知度框架,通过动态学习重分数函数的适应权重来提高因果性发现性能。在每样中,重量性能根据每个样本的重要性进行定量调整。我们用双级优化计划来训练标准的DAG学习员和再量性能样本,也就是,学习者无法适应和下调的样本。我们提议了一个简单性能模型,学习者可以轻易地提取重的线性能信息。我们从重的合成/直观性能分析中进行深入的实验,我们不断的合成和直观性能分析。我们不断的对数据进行。</s>