提出快速因果结构学习算法，显著提升效率与精度。
Embracing Discrete Search: A Reasonable Approach to Causal Structure Learning
- 结合快速父节点选择与基于乔列斯基的评分迭代更新
- 运行时间大幅缩短，标准场景下恢复准确率接近完美
- 适合需要高精度因果推断的研究者使用
我们提出FLOP（Fast Learning of Order and Parents），一种基于评分的线性模型因果发现算法。该算法将快速父节点选择与基于乔列斯基分解的评分迭代更新相结合，显著降低运行时间，相较以往方法有明显提升。这一效率改进使完全采用离散搜索成为可能，支持带合理顺序初始化的迭代局部搜索，从而找到接近全局最优解的因果图结构。在多个基准测试中，所得结构表现出极高的准确性，标准设置下的恢复精度接近完美。该结果表明，重新审视基于图的离散搜索作为因果发现的合理路径具有可行性。
原文摘要 · Abstract (English)
We present FLOP (Fast Learning of Order and Parents), a score-based causal discovery algorithm for linear models. It pairs fast parent selection with iterative Cholesky-based score updates, cutting run-times over prior algorithms. This makes it feasible to fully embrace discrete search, enabling iterated local search with principled order initialization to find graphs with scores at or close to the global optimum. The resulting structures are highly accurate across benchmarks, with near-perfect recovery in standard settings. This performance calls for revisiting discrete search over graphs as a reasonable approach to causal discovery.
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