Greedy Relaxations of the Sparsest Permutation Algorithm

• URL: http://arxiv.org/abs/2206.05421v1
• Date: Sat, 11 Jun 2022 05:00:36 GMT
• Title: Greedy Relaxations of the Sparsest Permutation Algorithm
• Authors: Wai-Yin Lam, Bryan Andrews, Joseph Ramsey
• Abstract summary: We 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.
• Score: 4.125187280299247
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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