Pith Number

pith:JHGIALL3

pith:2026:JHGIALL3BPPRTZFMGIBMHWY3AL

not attested not anchored not stored refs pending

ALM-MTA:Front-Door Causal Multi-Touch Attribution Method for Creator-Ecosystem Optimization

Han Li, Hu Liu, Jian Liang, Kun Gai, Luyao Xia, Yuguang Liu, Zhangxi Yan

Front-door identification with an adversarially learned mediator enables accurate multi-touch attribution from observational recommendation logs.

arxiv:2605.08881 v2 · 2026-05-09 · cs.SI

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\pithnumber{JHGIALL3BPPRTZFMGIBMHWY3AL}

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Record completeness

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4 Citations open

5 Replications open

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Claims

C1strongest claim

ALM-MTA increases DAU by 0.04% and daily active creators by 0.6%, with unit exposure efficiency increased by 670%. On causal utility, ALM-MTA achieves higher grouped AUUC than the SOTA in every propensity bucket, with a maximum gain of 0.070. In terms of accuracy, ALM-MTA improves upload AUC by 40% compared to SOTA.

C2weakest assumption

The adversarially learned mediator successfully distills outcome information to strengthen the causal pathway from treatment to outcome while eliminating shortcut leakage, and that contrastive learning on high-match pairs ensures positivity without introducing selection bias in the large treatment space.

C3one line summary

ALM-MTA uses front-door causal inference with an adversarially trained mediator and contrastive learning to improve multi-touch attribution, reporting gains in DAU, creator activity, exposure efficiency, AUUC, and upload AUC on a 400M DAU platform.

Receipt and verification

First computed	2026-05-26T01:03:32.972910Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

49cc802d7b0bdf19e4ac3202c3db1b02f3bff795485c3e1ff442c5db3e7f02ce

Aliases

arxiv: 2605.08881 · arxiv_version: 2605.08881v2 · doi: 10.48550/arxiv.2605.08881 · pith_short_12: JHGIALL3BPPR · pith_short_16: JHGIALL3BPPRTZFM · pith_short_8: JHGIALL3

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/JHGIALL3BPPRTZFMGIBMHWY3AL \
  | 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: 49cc802d7b0bdf19e4ac3202c3db1b02f3bff795485c3e1ff442c5db3e7f02ce

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "8899c42423ec67f7f92f804a7af65b58ad6bbc51a5a3c943d34ba9517d702c1d",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.SI",
    "submitted_at": "2026-05-09T11:04:18Z",
    "title_canon_sha256": "d036a9968f430590157c5dc68ec85ff490a8a02bc28fe9c4a2a04ba00021a496"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.08881",
    "kind": "arxiv",
    "version": 2
  }
}