Paper Citation Record · LEDGER

Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series

As of 8 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2607.24673.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.24673 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-31T08:17:35.702012Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

14 of 14 outbound references displayed

verified exact0
verified fuzzy0
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b96b2c4e-4b6a-4fde-bfce-224769845aa3 · outbound

This paper cites A distribution-free conditional independence test with applications to causal discovery.

Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series A distribution-free conditional independence test with applications to causal discovery

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-31T08:17:34.782950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0a8ddb8-cde9-496c-8604-d0e7b32e73bd · outbound

This paper cites CEDAR : Causal edge discovery for autoregressive processes.

Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series CEDAR : Causal edge discovery for autoregressive processes

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-31T08:17:34.856211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa5e0ea1-0f20-47e7-8119-abb0a3d7cd08 · outbound

This paper cites GRACE: Gated Refinement for Accurate Causal Edge Discovery in High-Dimensional Time Series.

Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series GRACE: Gated Refinement for Accurate Causal Edge Discovery in High-Dimensional Time Series

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-31T08:17:34.949746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a5cde20-dafe-49f7-afa0-42cdf9096460 · outbound

This paper cites Causal discovery from heterogeneous/nonstationary data.

Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series Causal discovery from heterogeneous/nonstationary data

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-31T08:17:35.016301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6645aeb5-1eaf-4cdd-b337-d5c3a733b684 · outbound

This paper cites an unresolved cited work.

Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series Unresolved cited work

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-31T08:17:35.075449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f1a2608-9463-40c5-ac87-48c066477779 · outbound

This paper cites Learning sparse neural networks through L_0 regularization.

Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series Learning sparse neural networks through L_0 regularization

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-31T08:17:35.162981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4511a1af-6cd9-4494-b354-bb0b1af3c08f · outbound

This paper cites Signature kernel conditional independence tests in causal discovery for stochastic processes.

Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series Signature kernel conditional independence tests in causal discovery for stochastic processes

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-31T08:17:35.223830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eae79960-3ddb-43c7-8705-3fcbdf897f40 · outbound

This paper cites Elements of Causal Inference: Foundations and Learning Algorithms.

Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series Elements of Causal Inference: Foundations and Learning Algorithms

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-31T08:17:35.281141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 635e5791-abdf-461a-a0f4-89ff6244d369 · outbound

This paper cites Sutherland, and Arthur Gretton.

Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series Sutherland, and Arthur Gretton

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-31T08:17:35.369174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8812dc87-7a87-4241-879a-57944199a5d6 · outbound

This paper cites Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information.

Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-31T08:17:35.425136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3326531e-b531-4012-b904-ba67fa120ff5 · outbound

This paper cites Detecting and quantifying causal associations in large nonlinear time series datasets.

Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series Detecting and quantifying causal associations in large nonlinear time series datasets

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-31T08:17:35.490775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee08909e-32c2-42ee-a86a-8849fa8913ab · outbound

This paper cites Causal discovery from nonstationary time series.

Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series Causal discovery from nonstationary time series

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-31T08:17:35.584721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c265d23e-122d-43d3-a398-b404c47dd065 · outbound

This paper cites DoWhy: An End-to-End Library for Causal Inference.

Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series DoWhy: An End-to-End Library for Causal Inference

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-31T08:17:35.647151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78fdfb1c-fc3d-477a-8dd7-e1eb962367cd · outbound

This paper cites Causal-learn: Causal discovery in P ython.

Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series Causal-learn: Causal discovery in P ython

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-31T08:17:35.702012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.