Pith Number

pith:AK6DJQZI

pith:2026:AK6DJQZIBEOOA4WRB3PALKEXNP

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PACER: Acyclic Causal Discovery from Large-Scale Interventional Data

Artyom Gadetsky, Ivo Alexander Ban, Maria Brbi\'c, Nikita Doikov, Ramon Vi\~nas Torn\'e, S\'ilvia F\`abregas Salazar, Soyon Park

PACER guarantees acyclicity by jointly modeling variable permutations and edge probabilities for direct optimization over valid causal structures.

arxiv:2605.15353 v1 · 2026-05-14 · cs.LG · cs.AI · q-bio.MN · q-bio.QM

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Claims

C1strongest claim

PACER guarantees acyclicity by construction through a joint model of variable permutations and edge probabilities, enabling direct optimization over valid causal structures and yielding up to two orders of magnitude speedups over penalty-based approaches while matching or exceeding state-of-the-art on protein signaling and large-scale genetic perturbation benchmarks.

C2weakest assumption

The parameterization over permutations and edge probabilities is assumed to efficiently cover the space of all DAGs without introducing bias or missing high-probability structures, particularly when incorporating structural prior knowledge or flexible conditional density models; this enters in the description of the joint model and the optimization procedure.

C3one line summary

PACER guarantees acyclicity by construction for causal discovery by jointly modeling permutations and edge probabilities, supporting unified treatment of observational and interventional data with closed-form expressions for linear-Gaussian cases.

References

12 extracted · 12 resolved · 0 Pith anchors

[1] Badia-i Mompel, P., Casals-Franch, R., Wessels, L., M¨uller- Dott, S., Trimbour, R., Yang, Y ., Ramirez Flores, R. O., and Saez-Rodriguez, J. Comparison and evaluation of methods to infer gene regulat 2024

[2] orders of magnitude larger and grows strongly with N (≈90 for N=500 vs 2022

[3] To complete the proof, we use the formulas,Var(aij) =E[a ij](1−E[aij]) and Cov(aij, akj) =E[a ij]E[akj] eθj eθi+eθj +eθk , that were established in Theorem C.4 2003

[4] NOTEARS (Zheng et al., 2018), NOTEARS-LR (Fang et al., 2023), DCDI (Brouillard et al., 2018

[5] Baselines implementations.GS, GES, GIES, IAMB, MMPC, GRaSP, BOSS, and LiNGAM are benchmarked using the implementation of the Causal Discovery Toolbox (Kalainathan et al., 2020) 2020

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:00:54.022076Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

02bc34c328091ce072d10ede05a8976bc01b58b6b99f73165656b4465a14e042

Aliases

arxiv: 2605.15353 · arxiv_version: 2605.15353v1 · doi: 10.48550/arxiv.2605.15353 · pith_short_12: AK6DJQZIBEOO · pith_short_16: AK6DJQZIBEOOA4WR · pith_short_8: AK6DJQZI

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/AK6DJQZIBEOOA4WRB3PALKEXNP \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 02bc34c328091ce072d10ede05a8976bc01b58b6b99f73165656b4465a14e042

Canonical record JSON

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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-14T19:26:53Z",
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