提出新算法学习含混杂变量的因果图，样本需求少十倍
ExMAG: Learning of Maximally Ancestral Graphs
- 用混合整数二次规划建模，结合分支定界法求解
- 在少量数据下重构准确率媲美或超越现有方法
- 适合处理存在隐变量的因果推断任务
在混合图中，同时存在有向边和双向边。将无环性扩展到此类混合图场景的概念称为最大祖先图，该概念在存在混杂因素的因果学习中具有重要意义。其中，有向边表示明确的因果方向，双向边表示混杂关系。本文提出一种基于混合整数二次规划的分支定界算法来学习最大祖先图。实验表明，该方法在重建质量上达到或优于当前最优方法，同时所需样本量减少一个数量级。
原文摘要 · Abstract (English)
In mixed graphs, there are both directed and bidirected edges. An extension of acyclicity to this mixed-graph setting is known as maximally ancestral graphs. This extension is of considerable interest in causal learning in the presence of confounders. There, directed edges represent a clear direction of causality, while bidirected edges represent confounding. We propose a branch-and-cut algorithm for learning maximally ancestral graphs using a formulation as a mixed-integer quadratic program. Empirically, our method achieves comparable or improved reconstruction quality while requiring an order of magnitude fewer samples than state-of-the-art approaches.
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