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Greedy Relaxations of the Sparsest Permutation Algorithm

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Wai-Yin Lam, Bryan Andrews, Joseph Ramsey

Artificial Intelligence · Submitted 11 Jun 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim 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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arXiv ID2206.05421 (version 1)
Pith IDDRYMB4RU, Pith's permanent record of this paper and its review
CitationBryan Andrews, Joseph Ramsey, Wai-Yin Lam. "Greedy Relaxations of the Sparsest Permutation Algorithm." arXiv:2206.05421 (2022). https://pith.science/paper/DRYMB4RU
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,071 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.10 · human participants 0.02 · controlled comparison 0.42 · randomized assignment 0.02 · states quantitative result 0.70 · compares with prior results 0.94 · mentions code or data availability 0.02.

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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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#sentencerolethe authors' own workstates the main conclusionstrength
s0 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. background 1.00 0.03 0.02 asserted 1.00
s1 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. contribution 0.99 0.95 0.20 asserted 1.00
s2 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. result 1.00 0.91 0.75 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.98.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original 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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