Pith Number

pith:AM6S5ZSY

pith:2025:AM6S5ZSYPW3GHRBIKNSVWFW2QL

not attested not anchored not stored refs pending

Evaluating and Learning Robust Bandit Policies Under Uncertain Causal Mechanisms

Chinmay Pendse, David Jensen, Katherine Avery

Structural equation models let bandit algorithms evaluate and learn policies accurately even when causal mechanisms remain uncertain.

arxiv:2508.02812 v3 · 2025-08-04 · cs.LG

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\pithnumber{AM6S5ZSYPW3GHRBIKNSVWFW2QL}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

the structural equation model (SEM) approach gives more accurate evaluations compared to traditional approaches, particularly as the range of possible causal mechanisms grows. Further, the SEM approach learns low-variance policies, and it learns an optimal policy, assuming the model is sufficiently well-specified.

C2weakest assumption

The structural equation model must be sufficiently well-specified for the method to learn an optimal policy; this premise is invoked in the abstract when stating convergence to optimality and is structurally required for the superiority claims to hold.

C3one line summary

A SEM-based causal bandit method provides more accurate policy evaluations and learns low-variance optimal policies under uncertain conditional distributions compared to traditional approaches.

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-06-02T03:04:33.706386Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

033d2ee6587db663c42853655b16da82e7d5c5cb7222e61b3a2ba2307a7c1f05

Aliases

arxiv: 2508.02812 · arxiv_version: 2508.02812v3 · doi: 10.48550/arxiv.2508.02812 · pith_short_12: AM6S5ZSYPW3G · pith_short_16: AM6S5ZSYPW3GHRBI · pith_short_8: AM6S5ZSY

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/AM6S5ZSYPW3GHRBIKNSVWFW2QL \
  | 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: 033d2ee6587db663c42853655b16da82e7d5c5cb7222e61b3a2ba2307a7c1f05

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "88bfa82c7e9f08f3743d01b81a74d78335ee0ec7f09259c2c861b22b3a807eaf",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2025-08-04T18:29:29Z",
    "title_canon_sha256": "a32b5b8e1bfd23cf337b2a11de4b74f5036b341e88069d50739200be95664d47"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2508.02812",
    "kind": "arxiv",
    "version": 3
  }
}