---
OA_place: publisher
OA_type: gold
_id: '14954'
abstract:
- lang: eng
text: "When domain knowledge is limited and experimentation is restricted by ethical,\r\nfinancial,
or time constraints, practitioners turn to observational causal discovery\r\nmethods
to recover the causal structure, exploiting the statistical properties of their\r\ndata.
Because causal discovery without further assumptions is an ill-posed problem,\r\neach
algorithm comes with its own set of usually untestable assumptions, some\r\nof
which are hard to meet in real datasets. Motivated by these considerations, this\r\npaper
extensively benchmarks the empirical performance of recent causal discovery\r\nmethods
on observational iid data generated under different background conditions,\r\nallowing
for violations of the critical assumptions required by each selected approach.
Our experimental findings show that score matching-based methods demonstrate surprising
performance in the false positive and false negative rate of the\r\ninferred graph
in these challenging scenarios, and we provide theoretical insights\r\ninto their
performance. This work is also the first effort to benchmark the stability of\r\ncausal
discovery algorithms with respect to the values of their hyperparameters. Finally,
we hope this paper will set a new standard for the evaluation of causal discovery
methods and can serve as an accessible entry point for practitioners interested\r\nin
the field, highlighting the empirical implications of different algorithm choices."
acknowledgement: "We thank Kun Zhang and Carl-Johann Simon-Gabriel for the insightful
discussions. This work\r\nhas been supported by AFOSR, grant n. FA8655-20-1-7035.
FM is supported by Programma\r\nOperativo Nazionale ricerca e innovazione 2014-2020.
FM partially contributed to this work during an internship at Amazon Web Services
with FL. FL partially contributed while at AWS."
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Francesco
full_name: Montagna, Francesco
last_name: Montagna
- first_name: Atalanti A.
full_name: Mastakouri, Atalanti A.
last_name: Mastakouri
- first_name: Elias
full_name: Eulig, Elias
last_name: Eulig
- first_name: Nicoletta
full_name: Noceti, Nicoletta
last_name: Noceti
- first_name: Lorenzo
full_name: Rosasco, Lorenzo
last_name: Rosasco
- first_name: Dominik
full_name: Janzing, Dominik
last_name: Janzing
- first_name: Bryon
full_name: Aragam, Bryon
last_name: Aragam
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Montagna F, Mastakouri AA, Eulig E, et al. Assumption violations in causal
discovery and the robustness of score matching. In: 37th Conference on Neural
Information Processing Systems. Vol 36. Neural Information Processing Systems
Foundation; 2023. doi:10.52202/075280-2050'
apa: 'Montagna, F., Mastakouri, A. A., Eulig, E., Noceti, N., Rosasco, L., Janzing,
D., … Locatello, F. (2023). Assumption violations in causal discovery and the
robustness of score matching. In 37th Conference on Neural Information Processing
Systems (Vol. 36). New Orleans, LO, United States: Neural Information Processing
Systems Foundation. https://doi.org/10.52202/075280-2050'
chicago: Montagna, Francesco, Atalanti A. Mastakouri, Elias Eulig, Nicoletta Noceti,
Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, and Francesco Locatello. “Assumption
Violations in Causal Discovery and the Robustness of Score Matching.” In 37th
Conference on Neural Information Processing Systems, Vol. 36. Neural Information
Processing Systems Foundation, 2023. https://doi.org/10.52202/075280-2050.
ieee: F. Montagna et al., “Assumption violations in causal discovery and
the robustness of score matching,” in 37th Conference on Neural Information
Processing Systems, New Orleans, LO, United States, 2023, vol. 36.
ista: 'Montagna F, Mastakouri AA, Eulig E, Noceti N, Rosasco L, Janzing D, Aragam
B, Locatello F. 2023. Assumption violations in causal discovery and the robustness
of score matching. 37th Conference on Neural Information Processing Systems. NeurIPS:
Neural Information Processing Systems, Advances in Neural Information Processing
Systems, vol. 36.'
mla: Montagna, Francesco, et al. “Assumption Violations in Causal Discovery and
the Robustness of Score Matching.” 37th Conference on Neural Information Processing
Systems, vol. 36, Neural Information Processing Systems Foundation, 2023,
doi:10.52202/075280-2050.
short: F. Montagna, A.A. Mastakouri, E. Eulig, N. Noceti, L. Rosasco, D. Janzing,
B. Aragam, F. Locatello, in:, 37th Conference on Neural Information Processing
Systems, Neural Information Processing Systems Foundation, 2023.
conference:
end_date: 2023-12-16
location: New Orleans, LO, United States
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2023-12-12
das_tickbox: '0'
date_created: 2024-02-07T15:11:56Z
date_published: 2023-12-20T00:00:00Z
date_updated: 2026-08-13T07:51:13Z
day: '20'
ddc:
- '000'
department:
- _id: FrLo
doi: 10.52202/075280-2050
external_id:
arxiv:
- '2310.13387'
file:
- access_level: open_access
checksum: 71d38402b4edef4c084b39f3e2f04526
content_type: application/pdf
creator: dernst
date_created: 2026-08-13T07:49:38Z
date_updated: 2026-08-13T07:49:38Z
file_id: '22701'
file_name: 2023_Neurips_Montagna.pdf
file_size: 7640984
relation: main_file
success: 1
file_date_updated: 2026-08-13T07:49:38Z
has_accepted_license: '1'
intvolume: ' 36'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
publication: 37th Conference on Neural Information Processing Systems
publication_identifier:
eissn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
researchdata_availability: no
status: public
supplementarymaterial: yes
title: Assumption violations in causal discovery and the robustness of score matching
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 36
year: '2023'
...