---
OA_place: repository
OA_type: green
_id: '20303'
abstract:
- lang: eng
  text: Observational genome-wide association studies are now widely used for causal
    inference in genetic epidemiology. To maintain privacy, such data is often only
    publicly available as summary statistics, and often studies for the endogenous
    covariates and the outcome are available separately. This has necessitated methods
    tailored to two-sample summary statistics. Current state-of-the-art methods modify
    linear instrumental variable (IV) regression---with genetic variants as instruments---to
    account for unmeasured confounding. However, since the endogenous covariates can
    be high dimensional, standard IV assumptions are generally insufficient to identify
    all causal effects simultaneously. We ensure identifiability by assuming the causal
    effects are sparse and propose a sparse causal effect two-sample IV estimator,
    spaceTSIV, adapting the spaceIV estimator by Pfister and Peters (2022) for two-sample
    summary statistics. We provide two methods, based on L0- and L1-penalization,
    respectively. We prove identifiability of the sparse causal effects in the two-sample
    setting and consistency of spaceTSIV. The performance of spaceTSIV is compared
    with existing two-sample IV methods in simulations. Finally, we showcase our methods
    using real proteomic and gene-expression data for drug-target discovery.
acknowledgement: "The authors would like to thank Stephen Burgess and Ashish Patel
  for helpful discussions at\r\nthe start of this research project, and Anton Rask
  Lundborg for helpful discussions on the\r\nuniform asymptotic results. This work
  was partially completed during SH’s research visit at\r\nNovo Nordisk. The authors
  would like to thank Jesper Ferkinghoff-Borg, Kang Li and Lewis\r\nMarsh for facilitating
  this visit and for discussing necessary concepts and tools in statistical\r\ngenetics
  at an early stage. SH and NP are supported by a research grant (0069071) from Novo\r\nNordisk
  Fonden. JB is funded at the University of Exeter by research grant MR/X011372/1."
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Shimeng
  full_name: Huang, Shimeng
  id: 989c2a06-fb4e-11ef-a992-ab766442255b
  last_name: Huang
  orcid: 0000-0001-6919-821X
- first_name: Niklas
  full_name: Pfister, Niklas
  last_name: Pfister
- first_name: Jack
  full_name: Bowden, Jack
  last_name: Bowden
citation:
  ama: 'Huang S, Pfister N, Bowden J. Sparse causal effect estimation using two-sample
    summary statistics in the presence of unmeasured confounding. In: <i>The 28th
    International Conference on Artificial Intelligence and Statistics</i>. Vol 258.
    ML Research Press; 2025:3394-3402.'
  apa: 'Huang, S., Pfister, N., &#38; Bowden, J. (2025). Sparse causal effect estimation
    using two-sample summary statistics in the presence of unmeasured confounding.
    In <i>The 28th International Conference on Artificial Intelligence and Statistics</i>
    (Vol. 258, pp. 3394–3402). Mai Khao, Thailand: ML Research Press.'
  chicago: Huang, Shimeng, Niklas Pfister, and Jack Bowden. “Sparse Causal Effect
    Estimation Using Two-Sample Summary Statistics in the Presence of Unmeasured Confounding.”
    In <i>The 28th International Conference on Artificial Intelligence and Statistics</i>,
    258:3394–3402. ML Research Press, 2025.
  ieee: S. Huang, N. Pfister, and J. Bowden, “Sparse causal effect estimation using
    two-sample summary statistics in the presence of unmeasured confounding,” in <i>The
    28th International Conference on Artificial Intelligence and Statistics</i>, Mai
    Khao, Thailand, 2025, vol. 258, pp. 3394–3402.
  ista: 'Huang S, Pfister N, Bowden J. 2025. Sparse causal effect estimation using
    two-sample summary statistics in the presence of unmeasured confounding. The 28th
    International Conference on Artificial Intelligence and Statistics. AISTATS: Conference
    on Artificial Intelligence and Statistics, PMLR, vol. 258, 3394–3402.'
  mla: Huang, Shimeng, et al. “Sparse Causal Effect Estimation Using Two-Sample Summary
    Statistics in the Presence of Unmeasured Confounding.” <i>The 28th International
    Conference on Artificial Intelligence and Statistics</i>, vol. 258, ML Research
    Press, 2025, pp. 3394–402.
  short: S. Huang, N. Pfister, J. Bowden, in:, The 28th International Conference on
    Artificial Intelligence and Statistics, ML Research Press, 2025, pp. 3394–3402.
conference:
  end_date: 2025-05-05
  location: Mai Khao, Thailand
  name: 'AISTATS: Conference on Artificial Intelligence and Statistics'
  start_date: 2025-05-03
date_created: 2025-09-07T22:01:35Z
date_published: 2025-05-01T00:00:00Z
date_updated: 2025-09-09T07:47:13Z
day: '01'
department:
- _id: FrLo
external_id:
  arxiv:
  - '2410.12300'
intvolume: '       258'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.48550/arXiv.2410.12300
month: '05'
oa: 1
oa_version: Preprint
page: 3394-3402
publication: The 28th International Conference on Artificial Intelligence and Statistics
publication_identifier:
  eissn:
  - 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Sparse causal effect estimation using two-sample summary statistics in the
  presence of unmeasured confounding
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 258
year: '2025'
...
---
OA_place: repository
OA_type: green
_id: '21068'
abstract:
- lang: eng
  text: "Causal reasoning and discovery, two fundamental tasks of causal analysis,\r\noften
    face challenges in applications due to the complexity, noisiness, and highdimensionality
    of real-world data. Despite recent progress in identifying latent\r\ncausal structures
    using causal representation learning (CRL), what makes learned\r\nrepresentations
    useful for causal downstream tasks and how to evaluate them are\r\nstill not well
    understood. In this paper, we reinterpret CRL using a measurement\r\nmodel framework,
    where the learned representations are viewed as proxy measurements of the latent
    causal variables. Our approach clarifies the conditions under\r\nwhich learned
    representations support downstream causal reasoning and provides\r\na principled
    basis for quantitatively assessing the quality of representations using\r\na new
    Test-based Measurement EXclusivity (T-MEX) score. We validate T-MEX\r\nacross
    diverse causal inference scenarios, including numerical simulations and\r\nreal-world
    ecological video analysis, demonstrating that the proposed framework\r\nand corresponding
    score effectively assess the identification of learned representations and their
    usefulness for causal downstream tasks. Reproducible code can\r\nbe found at https://github.com/shimenghuang/a-measurement-perspective-of-crl."
acknowledgement: "This research was funded in whole or in part by the Austrian Science
  Fund (FWF) 10.55776/COE12. For open access purposes, the author has applied a CC
  BY public copyright license to any accepted manuscript version arising from this
  submission.\r\n"
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Dingling
  full_name: Yao, Dingling
  id: d3e02e50-48a8-11ee-8f62-c108061797fa
  last_name: Yao
- first_name: Shimeng
  full_name: Huang, Shimeng
  id: 989c2a06-fb4e-11ef-a992-ab766442255b
  last_name: Huang
  orcid: 0000-0001-6919-821X
- first_name: Riccardo
  full_name: Cadei, Riccardo
  id: 0fa8b76f-72f0-11ef-b75a-a5da96e5ad6b
  last_name: Cadei
- first_name: Kun
  full_name: Zhang, Kun
  last_name: Zhang
- first_name: Francesco
  full_name: Locatello, Francesco
  id: 26cfd52f-2483-11ee-8040-88983bcc06d4
  last_name: Locatello
  orcid: 0000-0002-4850-0683
citation:
  ama: 'Yao D, Huang S, Cadei R, Zhang K, Locatello F. The third pillar of causal
    analysis? A measurement perspective on causal representations. In: <i>39th Annual
    Conference on Neural Information Processing Systems</i>. Vol 38. Neural Information
    Processing Systems Foundation; 2025. doi:<a href="https://doi.org/10.52202/085713-1444">10.52202/085713-1444</a>'
  apa: 'Yao, D., Huang, S., Cadei, R., Zhang, K., &#38; Locatello, F. (2025). The
    third pillar of causal analysis? A measurement perspective on causal representations.
    In <i>39th Annual Conference on Neural Information Processing Systems</i> (Vol.
    38). San Diego, CA, United States: Neural Information Processing Systems Foundation.
    <a href="https://doi.org/10.52202/085713-1444">https://doi.org/10.52202/085713-1444</a>'
  chicago: Yao, Dingling, Shimeng Huang, Riccardo Cadei, Kun Zhang, and Francesco
    Locatello. “The Third Pillar of Causal Analysis? A Measurement Perspective on
    Causal Representations.” In <i>39th Annual Conference on Neural Information Processing
    Systems</i>, Vol. 38. Neural Information Processing Systems Foundation, 2025.
    <a href="https://doi.org/10.52202/085713-1444">https://doi.org/10.52202/085713-1444</a>.
  ieee: D. Yao, S. Huang, R. Cadei, K. Zhang, and F. Locatello, “The third pillar
    of causal analysis? A measurement perspective on causal representations,” in <i>39th
    Annual Conference on Neural Information Processing Systems</i>, San Diego, CA,
    United States, 2025, vol. 38.
  ista: 'Yao D, Huang S, Cadei R, Zhang K, Locatello F. 2025. The third pillar of
    causal analysis? A measurement perspective on causal representations. 39th Annual
    Conference on Neural Information Processing Systems. NeurIPS: Neural Information
    Processing Systems, Advances in Neural Information Processing Systems, vol. 38.'
  mla: Yao, Dingling, et al. “The Third Pillar of Causal Analysis? A Measurement Perspective
    on Causal Representations.” <i>39th Annual Conference on Neural Information Processing
    Systems</i>, vol. 38, Neural Information Processing Systems Foundation, 2025,
    doi:<a href="https://doi.org/10.52202/085713-1444">10.52202/085713-1444</a>.
  short: D. Yao, S. Huang, R. Cadei, K. Zhang, F. Locatello, in:, 39th Annual Conference
    on Neural Information Processing Systems, Neural Information Processing Systems
    Foundation, 2025.
conference:
  end_date: 2025-12-07
  location: San Diego, CA, United States
  name: 'NeurIPS: Neural Information Processing Systems'
  start_date: 2025-12-02
corr_author: '1'
das_tickbox: '1'
date_created: 2026-01-29T14:24:56Z
date_published: 2025-12-15T00:00:00Z
date_updated: 2026-10-15T09:31:39Z
day: '15'
ddc:
- '000'
department:
- _id: FrLo
doi: 10.52202/085713-1444
external_id:
  arxiv:
  - '2505.17708'
fulldoi: https://doi.org/10.52202/085713-1444
has_accepted_license: '1'
intvolume: '        38'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.48550/arXiv.2505.17708
month: '12'
oa: 1
oa_version: Preprint
project:
- _id: a392d8f0-b034-11f1-b88e-d3025c6734f6
  grant_number: COE12
  name: Bilateral Artificial Intelligence (Locatello)
publication: 39th Annual Conference on Neural Information Processing Systems
publication_identifier:
  eissn:
  - 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
related_material:
  link:
  - relation: software
    url: https://github.com/shimenghuang/a-measurement-perspective-of-crl
status: public
title: The third pillar of causal analysis? A measurement perspective on causal representations
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 38
year: '2025'
...