pith:MP2GWTEH
Causal Learning with the Invariance Principle
Assuming acyclicity and invariance, only two auxiliary environments suffice to identify the causal graph for arbitrary nonlinear mechanisms.
arxiv:2605.13589 v1 · 2026-05-13 · stat.ML · cs.LG
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\usepackage{pith}
\pithnumber{MP2GWTEHNFOTOZC7NTK4JZOEXW}
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Record completeness
Claims
assuming that the causal relations are acyclic and invariant across multiple environments, only two auxiliary environments are sufficient to infer the causal graph for arbitrary nonlinear mechanisms
the causal relations are acyclic and invariant across multiple environments
Two auxiliary environments suffice to identify causal graphs and functional mechanisms in structural causal models under acyclicity and invariance assumptions, enabling correct counterfactual inference.
References
Receipt and verification
| First computed | 2026-05-18T02:44:23.098718Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
63f46b4c87695d37645f6cd5c4e5c4bdad5cd745a9e6bd7f1e1972ff63c8b5b7
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/MP2GWTEHNFOTOZC7NTK4JZOEXW \
| 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: 63f46b4c87695d37645f6cd5c4e5c4bdad5cd745a9e6bd7f1e1972ff63c8b5b7
Canonical record JSON
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],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "stat.ML",
"submitted_at": "2026-05-13T14:25:04Z",
"title_canon_sha256": "c414f7cde36d5f4c67635fb740b2e817e00bb6a108141474b652f85f3428e8bd"
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