Pith Number

pith:6UJ4OZBZ

pith:2026:6UJ4OZBZIXZAXKCGNKPJX7OKYA

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Learning Preferences from Conjoint Data: A Structural Deep Learning Approach

Avidit Acharya, Jens Hainmueller, Yiqing Xu

Embedding a deep neural network inside a random utility logit model recovers flexible preference heterogeneity from conjoint data.

arxiv:2604.10845 v2 · 2026-04-12 · stat.ME · econ.EM

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Claims

C1strongest claim

We propose a structural approach that embeds a deep neural network within a random utility logit model, allowing preference parameters to vary as a fully flexible function of respondent characteristics. [...] We apply our method to three prominent conjoint studies and find rich preference heterogeneity masked by reduced-form averages: a near-zero gender effect coexists with 83% preferring female candidates, opposition to undemocratic behavior is near-universal but varies sharply in intensity, and progressive tax preferences cut across every partisan subgroup.

C2weakest assumption

The assumption that the random utility logit model with neural network-embedded parameters accurately represents the choice process, and that double/debiased machine learning successfully debiases the estimates despite the high flexibility of the neural network.

C3one line summary

A structural deep learning approach for conjoint data reveals rich preference heterogeneity masked by reduced-form averages in three studies.

Cited by

1 paper in Pith

Ranked-choice conjoint experiments

Receipt and verification

First computed	2026-05-26T02:05:09.260459Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

f513c7643945f20ba8466a9e9bfdcac01f43b1352856769216428143c7bd9ebf

Aliases

arxiv: 2604.10845 · arxiv_version: 2604.10845v2 · doi: 10.48550/arxiv.2604.10845 · pith_short_12: 6UJ4OZBZIXZA · pith_short_16: 6UJ4OZBZIXZAXKCG · pith_short_8: 6UJ4OZBZ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/6UJ4OZBZIXZAXKCGNKPJX7OKYA \
  | 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: f513c7643945f20ba8466a9e9bfdcac01f43b1352856769216428143c7bd9ebf

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "aafd88124a2513206e8c5d6f7987e47577865866c671acf928aa5520f57a0a2e",
    "cross_cats_sorted": [
      "econ.EM"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "stat.ME",
    "submitted_at": "2026-04-12T22:35:04Z",
    "title_canon_sha256": "dcd6d07b75ca7a7e0d8a08f39ca341d8c7ef3854813863a1e3d69e6d6199ddad"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.10845",
    "kind": "arxiv",
    "version": 2
  }
}