---
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: 13th International Conference on
Learning Representations. ICLR; 2025.'
apa: 'Yao, D., Rancati, D., Cadei, R., Fumero, M., & Locatello, F. (2025). Unifying
causal representation learning with the invariance principle. In 13th International
Conference on Learning Representations. Singapore: ICLR.'
chicago: Yao, Dingling, Dario Rancati, Riccardo Cadei, Marco Fumero, and Francesco
Locatello. “Unifying Causal Representation Learning with the Invariance Principle.”
In 13th International Conference on Learning Representations. ICLR, 2025.
ieee: D. Yao, D. Rancati, R. Cadei, M. Fumero, and F. Locatello, “Unifying causal
representation learning with the invariance principle,” in 13th International
Conference on Learning Representations, 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.” 13th International Conference on Learning Representations,
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
checksum: c4b5a4a644228c6d1b0283e1368bce9e
content_type: application/pdf
creator: flocatel
date_created: 2026-01-27T12:43:25Z
date_updated: 2026-01-27T12:43:25Z
file_id: '21048'
file_name: 4356_Unifying_Causal_Represent (1).pdf
file_size: 877014
relation: main_file
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'
...
---
OA_place: publisher
OA_type: diamond
_id: '20032'
abstract:
- lang: eng
text: We propose Scalable Mechanistic Neural Network (S-MNN), an enhanced neural
network framework designed for scientific machine learning applications involving
long temporal sequences. By reformulating the original Mechanistic Neural Network
(MNN) (Pervez et al., 2024), we reduce the computational time and space complexities
from cubic and quadratic with respect to the sequence length, respectively, to
linear. This significant improvement enables efficient modeling of long-term dynamics
without sacrificing accuracy or interpretability. Extensive experiments demonstrate
that S-MNN matches the original MNN in precision while substantially reducing
computational resources. Consequently, S-MNN can drop-in replace the original
MNN in applications, providing a practical and efficient tool for integrating
mechanistic bottlenecks into neural network models of complex dynamical systems.
Source code is available at https://github.com/IST-DASLab/ScalableMNN.
article_processing_charge: No
arxiv: 1
author:
- first_name: Jiale
full_name: Chen, Jiale
id: 4d0a9064-1ff6-11ee-9fa6-ec046c604785
last_name: Chen
orcid: 0000-0001-5337-5875
- first_name: Dingling
full_name: Yao, Dingling
id: d3e02e50-48a8-11ee-8f62-c108061797fa
last_name: Yao
- first_name: Adeel A
full_name: Pervez, Adeel A
id: fca6d90c-d47f-11ee-bc87-93ff51604981
last_name: Pervez
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Chen J, Yao D, Pervez AA, Alistarh D-A, Locatello F. Scalable mechanistic
neural networks. In: 13th International Conference on Learning Representations.
ICLR; 2025:63716-63737.'
apa: 'Chen, J., Yao, D., Pervez, A. A., Alistarh, D.-A., & Locatello, F. (2025).
Scalable mechanistic neural networks. In 13th International Conference on Learning
Representations (pp. 63716–63737). Singapore, Singapore: ICLR.'
chicago: Chen, Jiale, Dingling Yao, Adeel A Pervez, Dan-Adrian Alistarh, and Francesco
Locatello. “Scalable Mechanistic Neural Networks.” In 13th International Conference
on Learning Representations, 63716–37. ICLR, 2025.
ieee: J. Chen, D. Yao, A. A. Pervez, D.-A. Alistarh, and F. Locatello, “Scalable
mechanistic neural networks,” in 13th International Conference on Learning
Representations, Singapore, Singapore, 2025, pp. 63716–63737.
ista: 'Chen J, Yao D, Pervez AA, Alistarh D-A, Locatello F. 2025. Scalable mechanistic
neural networks. 13th International Conference on Learning Representations. ICLR:
International Conference on Learning Representations, 63716–63737.'
mla: Chen, Jiale, et al. “Scalable Mechanistic Neural Networks.” 13th International
Conference on Learning Representations, ICLR, 2025, pp. 63716–37.
short: J. Chen, D. Yao, A.A. Pervez, D.-A. Alistarh, F. Locatello, in:, 13th International
Conference on Learning Representations, ICLR, 2025, pp. 63716–63737.
conference:
end_date: 2025-04-28
location: Singapore, Singapore
name: 'ICLR: International Conference on Learning Representations'
start_date: 2025-04-24
corr_author: '1'
date_created: 2025-07-20T22:02:01Z
date_published: 2025-04-01T00:00:00Z
date_updated: 2025-08-04T08:03:11Z
day: '01'
ddc:
- '000'
department:
- _id: DaAl
- _id: FrLo
external_id:
arxiv:
- '2410.06074'
file:
- access_level: open_access
checksum: 64cfdb12ae3e4e8ba57b1403e1066776
content_type: application/pdf
creator: dernst
date_created: 2025-07-22T07:58:22Z
date_updated: 2025-07-22T07:58:22Z
file_id: '20065'
file_name: 2025_ICLR_Chen.pdf
file_size: 732745
relation: main_file
success: 1
file_date_updated: 2025-07-22T07:58:22Z
has_accepted_license: '1'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
page: 63716-63737
publication: 13th International Conference on Learning Representations
publication_identifier:
isbn:
- '9798331320850'
publication_status: published
publisher: ICLR
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/IST-DASLab/ScalableMNN
scopus_import: '1'
status: public
title: Scalable mechanistic neural networks
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'
...
---
OA_place: repository
OA_type: green
_id: '20592'
abstract:
- lang: eng
text: "Filtering-based probabilistic numerical solvers for ordinary differential
equations (ODEs), also known as ODE filters, have been established as efficient
methods for quantifying numerical uncertainty in the solution of ODEs. In practical
applications, however, the underlying dynamical system often contains uncertain
parameters, requiring the propagation of this model uncertainty to the ODE solution.
In this paper, we demonstrate that ODE filters, despite their probabilistic nature,
do not automatically solve this uncertainty propagation problem. To address this
limitation, we present a novel approach that combines ODE filters with numerical
quadrature to properly marginalize over uncertain parameters, while accounting
for both parameter uncertainty and numerical solver uncertainty. Experiments across
multiple dynamical systems demonstrate that the resulting uncertainty estimates
closely match reference solutions. Notably, we show\r\nhow the numerical uncertainty
from the ODE solver can help prevent overconfidence in the propagated uncertainty
estimates, especially when using larger step sizes. Our results illustrate that
probabilistic numerical methods can effectively quantify both numerical and parametric
uncertainty in dynamical systems. "
acknowledgement: "NB gratefully acknowledge co-funding by the European Union (ERC,
ANUBIS, 101123955. Views and opinions expressed are however those of the author(s)
only and do not necessarily reflect those of the European Union or the European
Research Council. Neither the European Union nor the granting authority can be held
responsible for them). NB thanks the International\r\nMax Planck Research School
for Intelligent Systems (IMPRS-IS) for their support."
alternative_title:
- PMLR
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: Filip
full_name: Tronarp, Filip
last_name: Tronarp
- first_name: Nathanael
full_name: Bosch, Nathanael
last_name: Bosch
citation:
ama: 'Yao D, Tronarp F, Bosch N. Propagating model uncertainty through filtering-based
probabilistic numerical ODE solvers. In: Proceedings of the 1st International
Conference on Probabilistic Numerics. Vol 271. ML Research Press; 2025.'
apa: 'Yao, D., Tronarp, F., & Bosch, N. (2025). Propagating model uncertainty
through filtering-based probabilistic numerical ODE solvers. In Proceedings
of the 1st International Conference on Probabilistic Numerics (Vol. 271).
Sophia Antipolis, France: ML Research Press.'
chicago: Yao, Dingling, Filip Tronarp, and Nathanael Bosch. “Propagating Model Uncertainty
through Filtering-Based Probabilistic Numerical ODE Solvers.” In Proceedings
of the 1st International Conference on Probabilistic Numerics, Vol. 271. ML
Research Press, 2025.
ieee: D. Yao, F. Tronarp, and N. Bosch, “Propagating model uncertainty through filtering-based
probabilistic numerical ODE solvers,” in Proceedings of the 1st International
Conference on Probabilistic Numerics, Sophia Antipolis, France, 2025, vol.
271.
ista: 'Yao D, Tronarp F, Bosch N. 2025. Propagating model uncertainty through filtering-based
probabilistic numerical ODE solvers. Proceedings of the 1st International Conference
on Probabilistic Numerics. ProbNum: Conference on Probabilistic Numerics, PMLR,
vol. 271.'
mla: Yao, Dingling, et al. “Propagating Model Uncertainty through Filtering-Based
Probabilistic Numerical ODE Solvers.” Proceedings of the 1st International
Conference on Probabilistic Numerics, vol. 271, ML Research Press, 2025.
short: D. Yao, F. Tronarp, N. Bosch, in:, Proceedings of the 1st International Conference
on Probabilistic Numerics, ML Research Press, 2025.
conference:
end_date: 2025-09-03
location: Sophia Antipolis, France
name: 'ProbNum: Conference on Probabilistic Numerics'
start_date: 2025-09-01
date_created: 2025-11-02T23:01:35Z
date_published: 2025-01-01T00:00:00Z
date_updated: 2025-11-10T08:33:11Z
day: '01'
ddc:
- '000'
department:
- _id: FrLo
external_id:
arxiv:
- '2503.04684'
has_accepted_license: '1'
intvolume: ' 271'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-sa/4.0/
main_file_link:
- open_access: '1'
url: https://openreview.net/forum?id=sgPCP9jOlS
month: '01'
oa: 1
oa_version: Preprint
publication: Proceedings of the 1st International Conference on Probabilistic Numerics
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Propagating model uncertainty through filtering-based probabilistic numerical
ODE solvers
tmp:
image: /images/cc_by_sa.png
legal_code_url: https://creativecommons.org/licenses/by-sa/4.0/legalcode
name: Creative Commons Attribution-ShareAlike 4.0 International Public License (CC
BY-SA 4.0)
short: CC BY-SA (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 271
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: 39th Annual
Conference on Neural Information Processing Systems. Vol 38. Neural Information
Processing Systems Foundation; 2025.'
apa: 'Yao, D., Huang, S., Cadei, R., Zhang, K., & Locatello, F. (2025). The
third pillar of causal analysis? A measurement perspective on causal representations.
In 39th Annual Conference on Neural Information Processing Systems (Vol.
38). San Diego, CA, United States: Neural Information Processing Systems Foundation.'
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 39th Annual Conference on Neural Information Processing
Systems, Vol. 38. Neural Information Processing Systems Foundation, 2025.
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 39th
Annual Conference on Neural Information Processing Systems, 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.” 39th Annual Conference on Neural Information Processing
Systems, vol. 38, Neural Information Processing Systems Foundation, 2025.
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: 2026-07-28T07:14:27Z
day: '15'
ddc:
- '000'
department:
- _id: FrLo
external_id:
arxiv:
- '2505.17708'
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
publication: 39th Annual Conference on Neural Information Processing Systems
publication_identifier:
issn:
- 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'
...
---
OA_place: repository
OA_type: green
_id: '14946'
abstract:
- lang: eng
text: We present a unified framework for studying the identifiability of representations
learned from simultaneously observed views, such as different data modalities.
We allow a partially observed setting in which each view constitutes a nonlinear
mixture of a subset of underlying latent variables, which can be causally related.
We prove that the information shared across all subsets of any number of views
can be learned up to a smooth bijection using contrastive learning and a single
encoder per view. We also provide graphical criteria indicating which latent variables
can be identified through a simple set of rules, which we refer to as identifiability
algebra. Our general framework and theoretical results unify and extend several
previous work on multi-view nonlinear ICA, disentanglement, and causal representation
learning. We experimentally validate our claims on numerical, image, and multi-modal
data sets. Further, we demonstrate that the performance of prior methods is recovered
in different special cases of our setup. Overall, we find that access to multiple
partial views offers unique opportunities for identifiable representation learning,
enabling the discovery of latent structures from purely observational data.
acknowledgement: 'This work was initiated at the Second Bellairs Workshop on Causality
held at the Bellairs Research Institute, January 6–13, 2022; we thank all workshop
participants for providing a stimulating research environment. Further, we thank
Cian Eastwood, Luigi Gresele, Stefano Soatto, Marco Bagatella and A. René Geist
for helpful discussion. GM is a member of the Machine Learning Cluster of Excellence,
EXC number 2064/1 – Project number 390727645. JvK and GM acknowledge support from
the German Federal Ministry of Education and Research (BMBF) through the Tübingen
AI Center (FKZ: 01IS18039B). The research of DX and SM was supported by the Air
Force Office of Scientific Research under award number FA8655-22-1-7155. Any opinions,
findings, and conclusions or recommendations expressed in this material are those
of the author(s) and do not necessarily reflect the views of the United States Air
Force. We also thank SURF for the support in using the Dutch National Supercomputer
Snellius. SL was supported by an IVADO excellence PhD scholarship and by Samsung
Electronics Co., Ldt. DY was supported by an Amazon fellowship, the International
Max Planck Research School for Intelligent Systems (IMPRS-IS) and the ISTA graduate
school. Work done outside of Amazon.'
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: Danru
full_name: Xu, Danru
last_name: Xu
- first_name: Sébastien
full_name: Lachapelle, Sébastien
last_name: Lachapelle
- first_name: Sara
full_name: Magliacane, Sara
last_name: Magliacane
- first_name: Perouz
full_name: Taslakian, Perouz
last_name: Taslakian
- first_name: Georg
full_name: Martius, Georg
last_name: Martius
- first_name: Julius von
full_name: Kügelgen, Julius von
last_name: Kügelgen
- 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, Xu D, Lachapelle S, et al. Multi-view causal representation learning
with partial observability. In: 12th International Conference on Learning Representations.
Curran Associates; 2024.'
apa: 'Yao, D., Xu, D., Lachapelle, S., Magliacane, S., Taslakian, P., Martius, G.,
… Locatello, F. (2024). Multi-view causal representation learning with partial
observability. In 12th International Conference on Learning Representations.
Vienna, Austria: Curran Associates.'
chicago: Yao, Dingling, Danru Xu, Sébastien Lachapelle, Sara Magliacane, Perouz
Taslakian, Georg Martius, Julius von Kügelgen, and Francesco Locatello. “Multi-View
Causal Representation Learning with Partial Observability.” In 12th International
Conference on Learning Representations. Curran Associates, 2024.
ieee: D. Yao et al., “Multi-view causal representation learning with partial
observability,” in 12th International Conference on Learning Representations,
Vienna, Austria, 2024.
ista: 'Yao D, Xu D, Lachapelle S, Magliacane S, Taslakian P, Martius G, Kügelgen
J von, Locatello F. 2024. Multi-view causal representation learning with partial
observability. 12th International Conference on Learning Representations. ICLR:
International Conference on Learning Representations.'
mla: Yao, Dingling, et al. “Multi-View Causal Representation Learning with Partial
Observability.” 12th International Conference on Learning Representations,
Curran Associates, 2024.
short: D. Yao, D. Xu, S. Lachapelle, S. Magliacane, P. Taslakian, G. Martius, J.
von Kügelgen, F. Locatello, in:, 12th International Conference on Learning Representations,
Curran Associates, 2024.
conference:
end_date: 2024-05-07
location: Vienna, Austria
name: 'ICLR: International Conference on Learning Representations'
start_date: 2024-05-07
corr_author: '1'
date_created: 2024-02-07T14:28:34Z
date_published: 2024-11-07T00:00:00Z
date_updated: 2025-02-11T10:34:32Z
day: '07'
ddc:
- '000'
department:
- _id: FrLo
external_id:
arxiv:
- '2311.04056'
file:
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creator: dernst
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language:
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month: '11'
oa: 1
oa_version: Published Version
publication: 12th International Conference on Learning Representations
publication_status: published
publisher: Curran Associates
quality_controlled: '1'
status: public
title: Multi-view causal representation learning with partial observability
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...
---
OA_place: publisher
OA_type: gold
_id: '19005'
abstract:
- lang: eng
text: "Causal representation learning promises to extend causal models to hidden
causal\r\nvariables from raw entangled measurements. However, most progress has
focused\r\non proving identifiability results in different settings, and we are
not aware of any\r\nsuccessful real-world application. At the same time, the field
of dynamical systems\r\nbenefited from deep learning and scaled to countless applications
but does not allow\r\nparameter identification. In this paper, we draw a clear
connection between the two\r\nand their key assumptions, allowing us to apply
identifiable methods developed\r\nin causal representation learning to dynamical
systems. At the same time, we can\r\nleverage scalable differentiable solvers
developed for differential equations to build\r\nmodels that are both identifiable
and practical. Overall, we learn explicitly controllable models that isolate the
trajectory-specific parameters for further downstream\r\ntasks such as out-of-distribution
classification or treatment effect estimation. We\r\nexperiment with a wind simulator
with partially known factors of variation. We\r\nalso apply the resulting model
to real-world climate data and successfully answer\r\ndownstream causal questions
in line with existing literature on climate change.\r\nCode is available at https://github.com/CausalLearningAI/crl-dynamical-systems."
acknowledgement: "We thank Niklas Boers for recommending the SpeedyWeather simulator
and Valentino Maiorca\r\nfor guidance on Fourier transformation for SST data. We
are also grateful to Shimeng Huang and Riccardo Cadei for their feedback on the
treatment effect estimation experiment and to Jiale Chen and Adeel Pervez for their
assistance with the solver implementation. Finally, we appreciate the anonymous
reviewers for their insightful suggestions, which helped improve the manuscript. "
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: Caroline J
full_name: Muller, Caroline J
id: f978ccb0-3f7f-11eb-b193-b0e2bd13182b
last_name: Muller
orcid: 0000-0001-5836-5350
- 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, Muller CJ, Locatello F. Marrying causal representation learning with
dynamical systems for science. In: 38th Conference on Neural Information Processing
Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.'
apa: 'Yao, D., Muller, C. J., & Locatello, F. (2024). Marrying causal representation
learning with dynamical systems for science. In 38th Conference on Neural Information
Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing
Systems Foundation.'
chicago: Yao, Dingling, Caroline J Muller, and Francesco Locatello. “Marrying Causal
Representation Learning with Dynamical Systems for Science.” In 38th Conference
on Neural Information Processing Systems, Vol. 37. Neural Information Processing
Systems Foundation, 2024.
ieee: D. Yao, C. J. Muller, and F. Locatello, “Marrying causal representation learning
with dynamical systems for science,” in 38th Conference on Neural Information
Processing Systems, Vancouver, Canada, 2024, vol. 37.
ista: 'Yao D, Muller CJ, Locatello F. 2024. Marrying causal representation learning
with dynamical systems for science. 38th Conference on Neural Information Processing
Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information
Processing Systems, vol. 37.'
mla: Yao, Dingling, et al. “Marrying Causal Representation Learning with Dynamical
Systems for Science.” 38th Conference on Neural Information Processing Systems,
vol. 37, Neural Information Processing Systems Foundation, 2024.
short: D. Yao, C.J. Muller, F. Locatello, in:, 38th Conference on Neural Information
Processing Systems, Neural Information Processing Systems Foundation, 2024.
conference:
end_date: 2024-12-16
location: Vancouver, Canada
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2024-12-16
corr_author: '1'
date_created: 2025-02-05T07:49:00Z
date_published: 2024-12-01T00:00:00Z
date_updated: 2025-07-10T11:51:32Z
day: '01'
ddc:
- '000'
- '550'
department:
- _id: CaMu
- _id: FrLo
external_id:
arxiv:
- '2405.13888'
file:
- access_level: open_access
checksum: fe8832367e7143876f178244385d859e
content_type: application/pdf
creator: dernst
date_created: 2025-02-05T07:44:58Z
date_updated: 2025-02-05T07:44:58Z
file_id: '19006'
file_name: 2024_NeurIPS_Yao.pdf
file_size: 2595855
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success: 1
file_date_updated: 2025-02-05T07:44:58Z
has_accepted_license: '1'
intvolume: ' 37'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
publication: 38th Conference on Neural Information Processing Systems
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/CausalLearningAI/crl-dynamical-systems
scopus_import: '1'
status: public
title: Marrying causal representation learning with dynamical systems for science
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
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type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '14958'
abstract:
- lang: eng
text: Causal representation learning (CRL) aims at identifying high-level causal
variables from low-level data, e.g. images. Current methods usually assume that
all causal variables are captured in the high-dimensional observations. In this
work, we focus on learning causal representations from data under partial observability,
i.e., when some of the causal variables are not observed in the measurements,
and the set of masked variables changes across the different samples. We introduce
some initial theoretical results for identifying causal variables under partial
observability by exploiting a sparsity regularizer, focusing in particular on
the linear and piecewise linear mixing function case. We provide a theorem that
allows us to identify the causal variables up to permutation and element-wise
linear transformations in the linear case and a lemma that allows us to identify
causal variables up to linear transformation in the piecewise case. Finally, we
provide a conjecture that would allow us to identify the causal variables up to
permutation and element-wise linear transformations also in the piecewise linear
case. We test the theorem and conjecture on simulated data, showing the effectiveness
of our method.
acknowledgement: "This work was initiated at the Second Bellairs Workshop on Causality
held at the Bellairs Research Institute, January 6–13, 2022; we thank all workshop
participants for providing a stimulating research environment. The research of DX
and SM was supported by the Air Force Office of Scientific Research under award
number FA8655-22-1-7155. Any opinions, findings, and conclusions or recommendations
expressed in this material are those of the author(s) and do not necessarily reflect
the views of the United States Air Force. We also thank SURF for the support in
using the Dutch National Supercomputer Snellius. DY was supported by an Amazon fellowship
and the International Max Planck Research School for Intelligent Systems (IMPRS-IS).
Work done outside of Amazon. SL was supported by an IVADO excellence PhD scholarship
and by Samsung Electronics Co., Ldt. JvK acknowledges support from the German Federal
Ministry of Education and Research (BMBF)\r\nthrough the Tübingen AI Center (FKZ:
01IS18039B).\r\n"
article_number: '54'
article_processing_charge: No
author:
- first_name: Danru
full_name: Xu, Danru
last_name: Xu
- first_name: Dingling
full_name: Yao, Dingling
id: d3e02e50-48a8-11ee-8f62-c108061797fa
last_name: Yao
- first_name: Sebastien
full_name: Lachapelle, Sebastien
last_name: Lachapelle
- first_name: Perouz
full_name: Taslakian, Perouz
last_name: Taslakian
- first_name: Julius
full_name: von Kügelgen, Julius
last_name: von Kügelgen
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Sara
full_name: Magliacane, Sara
last_name: Magliacane
citation:
ama: 'Xu D, Yao D, Lachapelle S, et al. A sparsity principle for partially observable
causal representation learning. In: Causal Representation Learning Workshop
at NeurIPS 2023. OpenReview; 2023.'
apa: 'Xu, D., Yao, D., Lachapelle, S., Taslakian, P., von Kügelgen, J., Locatello,
F., & Magliacane, S. (2023). A sparsity principle for partially observable
causal representation learning. In Causal Representation Learning Workshop
at NeurIPS 2023. New Orleans, LA, United States: OpenReview.'
chicago: Xu, Danru, Dingling Yao, Sebastien Lachapelle, Perouz Taslakian, Julius
von Kügelgen, Francesco Locatello, and Sara Magliacane. “A Sparsity Principle
for Partially Observable Causal Representation Learning.” In Causal Representation
Learning Workshop at NeurIPS 2023. OpenReview, 2023.
ieee: D. Xu et al., “A sparsity principle for partially observable causal
representation learning,” in Causal Representation Learning Workshop at NeurIPS
2023, New Orleans, LA, United States, 2023.
ista: 'Xu D, Yao D, Lachapelle S, Taslakian P, von Kügelgen J, Locatello F, Magliacane
S. 2023. A sparsity principle for partially observable causal representation learning.
Causal Representation Learning Workshop at NeurIPS 2023. CRL: Causal Representation
Learning Workshop at NeurIPS, 54.'
mla: Xu, Danru, et al. “A Sparsity Principle for Partially Observable Causal Representation
Learning.” Causal Representation Learning Workshop at NeurIPS 2023, 54,
OpenReview, 2023.
short: D. Xu, D. Yao, S. Lachapelle, P. Taslakian, J. von Kügelgen, F. Locatello,
S. Magliacane, in:, Causal Representation Learning Workshop at NeurIPS 2023, OpenReview,
2023.
conference:
end_date: 2023-12-15
location: New Orleans, LA, United States
name: 'CRL: Causal Representation Learning Workshop at NeurIPS'
start_date: 2023-12-15
date_created: 2024-02-07T15:17:51Z
date_published: 2023-12-05T00:00:00Z
date_updated: 2025-02-04T12:37:34Z
day: '05'
ddc:
- '000'
department:
- _id: FrLo
file:
- access_level: open_access
checksum: 484efc27bda75ed6666044989695d9b6
content_type: application/pdf
creator: dernst
date_created: 2024-02-13T08:50:53Z
date_updated: 2024-02-13T08:50:53Z
file_id: '14982'
file_name: 2023_CRL_Xu.pdf
file_size: 552357
relation: main_file
success: 1
file_date_updated: 2024-02-13T08:50:53Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://openreview.net/forum?id=Whr6uobelR
month: '12'
oa: 1
oa_version: Published Version
publication: Causal Representation Learning Workshop at NeurIPS 2023
publication_status: published
publisher: OpenReview
quality_controlled: '1'
status: public
title: A sparsity principle for partially observable causal representation learning
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: '2023'
...