Pith Number

pith:3DGR3CLZ

pith:2026:3DGR3CLZFITYBIDL547QVNAM27

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Causal Multi-Task Demand Learning

Varun Gupta, Vijay Kamble

A meta-learning framework identifies causal demand parameters across tasks by conditioning on all prices while masking two outcomes for supervision.

arxiv:2602.09969 v2 · 2026-02-10 · cs.LG · econ.EM · stat.ML

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3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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Claims

C1strongest claim

We propose a new meta-learning framework that identifies the conditional mean of task-specific causal demand parameters given a subset of task-specific observables despite such confounding, assuming that each task contains at least two distinct locally exogenous price points.

C2weakest assumption

Each task contains at least two distinct locally exogenous price points, and the proposed information design (conditioning on all prices while masking two outcomes) is maximally uniformly valid.

C3one line summary

A meta-learning method identifies the conditional mean of task-specific causal demand parameters by conditioning on all prices while masking two demand outcomes, assuming at least two locally exogenous prices per task.

References

13 extracted · 13 resolved · 0 Pith anchors

[1] Steven Berry, James Levinsohn, and Ariel Pakes 1991 · doi:10.1109/ijcnn.1991.155621

[2] doi: 10.1201/9781420057669 · doi:10.1201/9781420057669

[3] Online Retail.https://doi.org/10.24432/C5BW33 · doi:10.24432/c5bw33

[4] Applied Causal Inference Powered by ML and AI

[5] doi: 10.1017/CBO9780511761362 · doi:10.1017/cbo9780511761362

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:16.233532Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

d8cd1d89792a2780a06bef3f0ab40cd7c177c7f9b3767c5f458df7e0d860cacf

Aliases

arxiv: 2602.09969 · arxiv_version: 2602.09969v2 · doi: 10.48550/arxiv.2602.09969 · pith_short_12: 3DGR3CLZFITY · pith_short_16: 3DGR3CLZFITYBIDL · pith_short_8: 3DGR3CLZ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/3DGR3CLZFITYBIDL547QVNAM27 \
  | 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: d8cd1d89792a2780a06bef3f0ab40cd7c177c7f9b3767c5f458df7e0d860cacf

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "bb9f37e247bd397f1ac594e94565b86db20b7211d093c9aa62d8b98e59beab49",
    "cross_cats_sorted": [
      "econ.EM",
      "stat.ML"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-02-10T16:58:50Z",
    "title_canon_sha256": "ac653119b1e9c4e9914bd3a258b460c4fd335d80e8e073792ac7415e98619064"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2602.09969",
    "kind": "arxiv",
    "version": 2
  }
}