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pith:E7FQG7PC

pith:2026:E7FQG7PC6B63OEY3DTYXDCLGDZ

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IV-ICL: Bounding Causal Effects with Instrumental Variables via In-Context Learning

Hamidreza Kamkari, Medha Barath, Rahul G. Krishnan, Ricardo Silva, Vahid Balazadeh

An amortized in-context learner recovers the full identified set of causal effects from instrumental variable data.

arxiv:2605.12924 v1 · 2026-05-13 · cs.LG

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Claims

C1strongest claim

We introduce IV-ICL, an amortized Bayesian in-context learning method that learns the marginal posterior distribution of the causal effects directly and derives bounds as its quantiles. ... optimizing inclusive KL can recover the entire identified set across diverse data-generating processes, while exclusive-KL ... collapses onto a single mode.

C2weakest assumption

That the inclusive-KL objective in the amortized in-context learner will reliably recover the full identified set for arbitrary data-generating processes rather than only for the synthetic distributions used in training.

C3one line summary

IV-ICL learns the marginal posterior of causal effects via in-context learning to derive bounds as quantiles, recovering the identified set more reliably than variational inference while running 20-500x faster.

References

75 extracted · 75 resolved · 1 Pith anchors

[1] Accountability and flexibility in public schools: Evidence from boston’s charters and pilots.The Quarterly Journal of Economics, 126(2):699–748, 2011 2011

[2] Joshua D Angrist and Guido W. Imbens. Identification and estimation of local average treatment effects.Econometrica, 62:467–475, 1994 1994

[3] Princeton university press, 2009 2009

[4] Identification of causal effects using instrumental variables.Journal of the American statistical Association, 91(434):444–455, 1996 1996

[5] The paired availability design: a proposal for evaluating epidural analgesia during labor.Statistics in medicine, 13(21):2269–2278, 1994 1994

Receipt and verification

First computed	2026-05-18T03:09:10.136109Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

27cb037de2f07db7131b1cf17189661e68809cd2f1908e1b563a85d91ccf5c15

Aliases

arxiv: 2605.12924 · arxiv_version: 2605.12924v1 · doi: 10.48550/arxiv.2605.12924 · pith_short_12: E7FQG7PC6B63 · pith_short_16: E7FQG7PC6B63OEY3 · pith_short_8: E7FQG7PC

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/E7FQG7PC6B63OEY3DTYXDCLGDZ \
  | 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: 27cb037de2f07db7131b1cf17189661e68809cd2f1908e1b563a85d91ccf5c15

Canonical record JSON

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  "metadata": {
    "abstract_canon_sha256": "325cb2fb8dd13b92ae7c371449d3d7645808a2d2ea13c6b147ae254c4d6178de",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-13T03:00:08Z",
    "title_canon_sha256": "bc030469ff6725ab1fd0853ba1dc7ebffc386d8c5301c47cc181f318e40720a3"
  },
  "schema_version": "1.0",
  "source": {
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    "kind": "arxiv",
    "version": 1
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}