Papers › CUTS+: High-dimensional Causal Discovery from Irregular Time-series

CUTS+: High-dimensional Causal Discovery from Irregular Time-series

10 May 2023arXiv:2305.05890archive 2025-07-28

Yuxiao Cheng, Lianglong Li, Tingxiong Xiao, Zongren Li, Qin Zhong, Jinli Suo, Kunlun He

Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios. Recently, researchers successfully discover causality by combining neural networks with Granger causality, but their performances degrade largely when encountering high-dimensional data because of the highly redundant network design and huge causal graphs. Moreover, the missing entries in the observations further hamper the causal structural learning. To overcome these limitations, We propose CUTS+, which is built on the Granger-causality-based causal discovery method CUTS and raises the scalability by introducing a technique called Coarse-to-fine-discovery (C2FD) and leveraging a message-passing-based graph neural network (MPGNN). Compared to previous methods on simulated, quasi-real, and real datasets, we show that CUTS+ largely improves the causal discovery performance on high-dimensional data with different types of irregular sampling.

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2305.05890")

Code

Syntology Ran 8 of 8 code samples harvested from 1 repository linked to this paper; 0 have no recorded run. Of those that ran: 2 ran · honoured contract; 3 ran · our draft was wrong; 3 ran · fixture could not drive it.

By repository: official repository: 6 samples from 1 repository, 6 ran; 2 identical to code first harvested elsewhere. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

jarrycyx/unn officialmentioned in papermentioned on GitHubpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

8 samples harvested; 8 ran; 2 honoured the contract we drafted; 0 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

2ran · honoured contract
3ran · our draft was wrong
3ran · fixture could not drive it

Licence: 2 of the 8 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from jarrycyx/unn. Some samples are identical code Syntology first harvested from another repository; for those, this paper's copy is not located and its licence is not recorded. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

check_stationarity jarrycyx/unn/CUTS_Plus/data/generate_data_mod.py official repository ran · honoured contract MIT (permissive) · 2e1e2740647f3438 · report
find_lineprofile_cmp jarrycyx/unn/CUTS_Plus/utils/exp_utils.py official repository ran · fixture could not drive it MIT (permissive) · 1773694b64273196 · report
generate_nonlinear_contemp_timeseries jarrycyx/unn/CUTS_Plus/data/generate_data_mod.py official repository ran · our draft was wrong MIT (permissive) · 6825deafa85f9ef0 · report
generate_random_contemp_model jarrycyx/unn/CUTS_Plus/data/generate_data_mod.py official repository ran · fixture could not drive it MIT (permissive) · 4077d55cef2b6736 · report
get_decompressed_path jarrycyx/unn/CUTS_Plus/utils/exp_utils.py official repository ran · our draft was wrong MIT (permissive) · f54cadf641fb2f2e · report
name jarrycyx/unn/CUTS_Plus/utils/exp_utils.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · f61b343a30e8ea11 · report
generate_indices identical code first harvested elsewhere ran · honoured contract fingerprinted licence of this copy not recorded · 5c86959f0283a76a · report
prepross_data identical code first harvested elsewhere ran · fixture could not drive it fingerprinted licence of this copy not recorded · 923cb04dc471f021 · report

Tasks

Causal DiscoveryDecision MakingGraph Neural NetworkIrregular Time SeriesTime SeriesVocal Bursts Intensity Prediction

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Methods

Graph Neural Network

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections