Greedy Relaxations of the Sparsest Permutation Algorithm

Wai-Yin Lam, Bryan Andrews, Joseph Ramsey

Keywords: causal discovery, permutation, causal faithfulness condition

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 computationally 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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