论文标题
最稀少的排列算法的贪婪放松
Greedy Relaxations of the Sparsest Permutation Algorithm
论文作者
论文摘要
人们对利用置换推理的方法越来越感兴趣，以搜索有针对性的环保因子模型，包括Teysier的“订购搜索”，Kohler和Solus，Wang和Uhler的GSP。我们通过基于置换的操作Tuck扩展了后者的方法，并开发了一类算法，即掌握，这些算法在越来越弱的假设下比忠诚度更有效且偶然地保持一致。最放松的掌握形式优于模拟中许多最新的因果搜索算法，即使对于具有超过100个变量的密集图和图形，也可以有效，准确地搜索。
There has been an increasing interest in methods that exploit permutation reasoning to search for directed acyclic causal models, including the "Ordering Search" of Teyssier and Kohler and GSP of Solus, Wang and Uhler. We extend the methods of the latter by a permutation-based operation, tuck, and develop a class of algorithms, namely GRaSP, that are efficient and pointwise consistent under increasingly weaker assumptions than faithfulness. The most relaxed form of GRaSP outperforms many state-of-the-art causal search algorithms in simulation, allowing efficient and accurate search even for dense graphs and graphs with more than 100 variables.
