---
OA_place: publisher
OA_type: gold
_id: '19010'
abstract:
- lang: eng
  text: Causal representation learning aims at recovering latent causal variables
    from high-dimensional observations to solve causal downstream tasks, such as predicting
    the effect of new interventions or more robust classification. A plethora of methods
    have been developed, each tackling carefully crafted problem settings that lead
    to different types of identifiability. The folklore is that these different settings
    are important, as they are often linked to different rungs of Pearl's causal hierarchy,
    although not all neatly fit. Our main contribution is to show that many existing
    causal representation learning approaches methodologically align the representation
    to known data symmetries. Identification of the variables is guided by equivalence
    classes across different "data pockets" that are not necessarily causal. This
    result suggests important implications, allowing us to unify many existing approaches
    in a single method that can mix and match different assumptions, including non-causal
    ones, based on the invariances relevant to our application. It also significantly
    benefits applicability, which we demonstrate by improving treatment effect estimation
    on real-world high-dimensional ecological data. Overall, this paper clarifies
    the role of causality assumptions in the discovery of causal variables and shifts
    the focus to preserving data symmetries.
acknowledgement: "We thank Jiaqi Zhang, Francesco Montagna, David Lopez-Paz, Kartik
  Ahuja, Thomas Kipf, Sara\r\nMagliacane, Julius von Kügelgen, Kun Zhang, and Bernhard
  Schölkopf for extremely helpful discussion. Riccardo Cadei was supported by a Google
  Research Scholar Award to Francesco Locatello. We acknowledge the Third Bellairs
  Workshop on Causal Representation Learning held at the Bellairs Research Institute,
  February 9/16, 2024, and a debate on the difference between interventions and counterfactuals
  in disentanglement and CRL that took place during Dhanya Sridhar’s lecture, which
  motivated us to significantly broaden the scope of the paper. We thank Dhanya and
  all participants of the workshop."
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: Dario
  full_name: Rancati, Dario
  id: feb58f2e-72ef-11ef-b75a-8f0894539cd0
  last_name: Rancati
- first_name: Riccardo
  full_name: Cadei, Riccardo
  id: 0fa8b76f-72f0-11ef-b75a-a5da96e5ad6b
  last_name: Cadei
- first_name: Marco
  full_name: Fumero, Marco
  id: 1c1593eb-393f-11ef-bb8e-ab4f1e979650
  last_name: Fumero
- 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, Rancati D, Cadei R, Fumero M, Locatello F. Unifying causal representation
    learning with the invariance principle. In: <i>13th International Conference on
    Learning Representations</i>. ICLR; 2025.'
  apa: 'Yao, D., Rancati, D., Cadei, R., Fumero, M., &#38; Locatello, F. (2025). Unifying
    causal representation learning with the invariance principle. In <i>13th International
    Conference on Learning Representations</i>. Singapore: ICLR.'
  chicago: Yao, Dingling, Dario Rancati, Riccardo Cadei, Marco Fumero, and Francesco
    Locatello. “Unifying Causal Representation Learning with the Invariance Principle.”
    In <i>13th International Conference on Learning Representations</i>. ICLR, 2025.
  ieee: D. Yao, D. Rancati, R. Cadei, M. Fumero, and F. Locatello, “Unifying causal
    representation learning with the invariance principle,” in <i>13th International
    Conference on Learning Representations</i>, Singapore, 2025.
  ista: 'Yao D, Rancati D, Cadei R, Fumero M, Locatello F. 2025. Unifying causal representation
    learning with the invariance principle. 13th International Conference on Learning
    Representations. ICLR: International Conference on Learning Representations.'
  mla: Yao, Dingling, et al. “Unifying Causal Representation Learning with the Invariance
    Principle.” <i>13th International Conference on Learning Representations</i>,
    ICLR, 2025.
  short: D. Yao, D. Rancati, R. Cadei, M. Fumero, F. Locatello, in:, 13th International
    Conference on Learning Representations, ICLR, 2025.
conference:
  end_date: 2025-04-28
  location: Singapore
  name: 'ICLR: International Conference on Learning Representations'
  start_date: 2025-04-24
corr_author: '1'
date_created: 2025-02-05T09:23:25Z
date_published: 2025-01-22T00:00:00Z
date_updated: 2026-02-09T05:52:14Z
day: '22'
ddc:
- '000'
department:
- _id: FrLo
external_id:
  arxiv:
  - '2409.02772'
file:
- access_level: open_access
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  content_type: application/pdf
  creator: flocatel
  date_created: 2026-01-27T12:43:25Z
  date_updated: 2026-01-27T12:43:25Z
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  success: 1
file_date_updated: 2026-01-27T12:43:25Z
has_accepted_license: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '01'
oa: 1
oa_version: Published Version
publication: 13th International Conference on Learning Representations
publication_status: published
publisher: ICLR
quality_controlled: '1'
scopus_import: '1'
status: public
title: Unifying causal representation learning with the invariance principle
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
year: '2025'
...