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.

翻译：我们提出FLOP（快速学习顺序与父节点），一种面向线性模型的基于评分的因果发现算法。该算法将快速的父节点选择与基于迭代Cholesky分解的评分更新相结合，显著降低了运行时间。这使得完全采用离散搜索成为可能，通过基于原则的顺序初始化进行迭代局部搜索，从而找到评分达到或接近全局最优的图结构。在基准测试中，所得结构具有极高的准确性，在标准设置下近乎完美恢复。这一性能表明，有必要重新审视基于图的离散搜索作为因果发现的一种合理方法。