Pith Number

pith:OL36OEYJ

pith:2026:OL36OEYJROTAHK2XFVJZQQKPDH

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A Practical Guide to Instrumental Variables Methods with Heterogeneous Treatment Effects

Liyang Sun, S. Derya Uysal, Tymon S{\l}oczy\'nski

Different specifications for covariates in instrumental variables regressions produce distinct weighted averages of covariate-specific local average treatment effects.

arxiv:2605.15115 v1 · 2026-05-14 · econ.EM · stat.ME

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Claims

C1strongest claim

different specifications with covariates lead to distinct weighted averages of covariate-specific LATEs. Parametric misspecification can undermine the causal interpretation of these estimands.

C2weakest assumption

That researchers will correctly implement and interpret the recommended flexible specifications and tests in their specific applications, and that the underlying LATE assumptions are plausible in the data.

C3one line summary

A synthesis of how covariate-inclusive IV specifications produce weighted averages of subgroup LATEs, with recommendations for flexible models, assumption tests, and software to handle heterogeneous effects.

References

55 extracted · 55 resolved · 1 Pith anchors

[1] Abadie, A. (2003). Semiparametric instrumental variable estimation of treatment response models. Journal of Econometrics , 113(2):231--263 2003

[2] Anatolyev, S. (2019). Many instruments and/or regressors: A friendly guide. Journal of Economic Surveys , 33(2):689--726 2019

[3] Andresen, M. E. (2026). montest: T esting LATE assumptions and monotonicity using machine learning. https://github.com/martin-andresen/montest 2026

[4] Angrist, J. D. and Imbens, G. W. (1995). Two-stage least squares estimation of average causal effects in models with variable treatment intensity. Journal of the American Statistical Association , 90( 1995

[5] Angrist, J. D., Imbens, G. W., and Krueger, A. B. (1999). Jackknife instrumental variables estimation. Journal of Applied Econometrics , 14(1):57--67 1999

Receipt and verification

First computed	2026-05-17T21:40:25.726039Z
Last reissued	2026-05-17T21:57:19.062266Z
Builder	pith-number-builder-2026-05-17-v1
Signature	unsigned_v0
Schema	pith-number/v1.0

Canonical hash

72f7e713098ba603ab572d5398414f19f03acad55c7e07801710ec0fcbb78e8f

Aliases

arxiv: 2605.15115 · arxiv_version: 2605.15115v1 · pith_short_12: OL36OEYJROTA · pith_short_16: OL36OEYJROTAHK2X · pith_short_8: OL36OEYJ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/OL36OEYJROTAHK2XFVJZQQKPDH \
  | 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: 72f7e713098ba603ab572d5398414f19f03acad55c7e07801710ec0fcbb78e8f

Canonical record JSON

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    "abstract_canon_sha256": "d83112655fbac06f3d80c00a22c1f03de5302064ed86c0829d1f542dde6aeb5f",
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      "stat.ME"
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "econ.EM",
    "submitted_at": "2026-05-14T17:29:26Z",
    "title_canon_sha256": "19840493a0f2b1f5d32276aaebdbba3614f2f00ea8edf294f973dafebdf7f42a"
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    "kind": "arxiv",
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