标题： 拥抱离散搜索：因果结构学习的一种合理方法 显示英文标题

标题： Embracing Discrete Search: A Reasonable Approach to Causal Structure Learning

摘要： 我们提出FLOP（快速学习顺序和父节点），一种基于评分的因果发现算法，适用于线性模型。 它将快速父节点选择与迭代Cholesky基评分更新相结合，显著减少了运行时间，优于之前算法。 这使得全面采用离散搜索成为可能，从而实现带有合理顺序初始化的迭代局部搜索，以找到得分接近全局最优的图结构。 在基准测试中，所得结构具有高度准确性，在标准设置中实现了近乎完美的恢复。 这种性能促使我们重新考虑在图上进行离散搜索作为因果发现的一种合理方法。 显示英文摘要

摘要： 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.