Paper Citation Record · LEDGER

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes

As of 8 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2507.11178.

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

pith.paper-citation-record.v1
2507.11178 v3

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:23:49.307429Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:18:49.948480Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T15:18:53.068505Z

Reference resolution

27 of 27 outbound references displayed

verified exact0
verified fuzzy26
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f81d2182-40e2-4079-869e-1025a559ac1e · outbound

This paper cites Regioselective hydroformylation of propene catalysed by rhodium-zeolite,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Regioselective hydroformylation of propene catalysed by rhodium-zeolite,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:52.973579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d79f06af-bedb-434e-ab08-c1d0595dfd7b · outbound

This paper cites Causal-trivial attention graph neural network for fault diagnosis of complex industrial processes,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Causal-trivial attention graph neural network for fault diagnosis of complex industrial processes,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:52.838926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d063b0ef-9c29-4948-a259-5e83e352326a · outbound

This paper cites Investigating causal relations by econometric models and cross-spectral methods,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Investigating causal relations by econometric models and cross-spectral methods,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:52.694177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c3f9d239-5611-432f-8122-69d58be44348 · outbound

This paper cites An integrated distributed fault diagnosis framework for large-scale industrial processes based on spatio–temporal causal analysis,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes An integrated distributed fault diagnosis framework for large-scale industrial processes based on spatio–temporal causal analysis,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:52.527483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 40168e5c-6ef8-4f81-8c71-fbc8897ae58a · outbound

This paper cites Attribution-aided non- linear granger causality discovery method and its industrial application,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Attribution-aided non- linear granger causality discovery method and its industrial application,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:52.399440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8c316960-5bb6-4bf5-8bf2-19eb38243288 · outbound

This paper cites Causal inference for time series,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Causal inference for time series,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:52.245704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 38690283-2231-47fa-9aea-80a5080e340d · outbound

This paper cites A spatiotemporal causal model for revealing developmen- tal changes in infants’ brain effective connectivity networks during the first year of life,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes A spatiotemporal causal model for revealing developmen- tal changes in infants’ brain effective connectivity networks during the first year of life,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:52.120889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6f6a4eab-ed7f-4392-8851-a8ed6fd901e4 · outbound

This paper cites Neural granger causality,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Neural granger causality,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T17:23:47.520774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 062a52ec-1e13-44a4-8102-8c217773a8ed · outbound

This paper cites Economy statistical recurrent units for inferring nonlinear granger causality,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Economy statistical recurrent units for inferring nonlinear granger causality,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:51.917444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ded67a56-5288-49d9-bd13-358bc3c08d65 · outbound

This paper cites Cuts: Neural causal discovery from irregular time-series data,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Cuts: Neural causal discovery from irregular time-series data,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:51.790895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dc3b9adc-66d1-4add-a7ee-563d092887da · outbound

This paper cites Granger causality using jacobian in neural networks,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Granger causality using jacobian in neural networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:51.673845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b4bbed99-4897-487f-b55c-d6a6eaaee222 · outbound

This paper cites Cuts+: High- dimensional causal discovery from irregular time-series,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Cuts+: High- dimensional causal discovery from irregular time-series,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:51.530695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e4105eff-c310-4fa5-b22b-b121e537532c · outbound

This paper cites Neural additive vector autoregres- sion models for causal discovery in time series,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Neural additive vector autoregres- sion models for causal discovery in time series,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:51.401469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1ac1cb3d-d2be-4745-8adc-e128a31a018f · outbound

This paper cites Causal discovery with attention- based convolutional neural networks,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Causal discovery with attention- based convolutional neural networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:51.259415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cbc597a9-f43b-4d58-be8f-b221f48b1467 · outbound

This paper cites Interpretable models for granger causal- ity using self-explaining neural networks,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Interpretable models for granger causal- ity using self-explaining neural networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:51.139256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2abdb047-eae2-4911-b5d5-cc7e7bb2591f · outbound

This paper cites Causal recurrent variational autoencoder for medical time series generation,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Causal recurrent variational autoencoder for medical time series generation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:50.991127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 793bdfd4-d3d0-4c91-a241-974fad8a1eec · outbound

This paper cites An information- theoretic approach for heterogeneous differentiable causal discovery,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes An information- theoretic approach for heterogeneous differentiable causal discovery,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:50.845673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b7576e2d-b0e9-480a-a01e-b0c830932fbb · outbound

This paper cites Causality detection with matrix-based transfer entropy,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Causality detection with matrix-based transfer entropy,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:50.742566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 81a77687-e6df-4412-8a07-5b9d6bda4ca6 · outbound

This paper cites Surrogates with random fourier phases,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Surrogates with random fourier phases,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:50.589571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 75f795ed-da4d-4074-96c4-338f9a21cbc2 · outbound

This paper cites Detection of the number of signals using the benjamini-hochberg procedure,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Detection of the number of signals using the benjamini-hochberg procedure,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:50.430039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0b6f4d02-7a3b-4c6f-9e79-14cc6fbe302d · outbound

This paper cites Dream3: network inference using dynamic context likelihood of relatedness and the inferelator,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Dream3: network inference using dynamic context likelihood of relatedness and the inferelator,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:50.288066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e25eb821-31fb-4b0e-b510-a475da27b26e · outbound

This paper cites Extensive chaos in the lorenz-96 model,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Extensive chaos in the lorenz-96 model,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:50.138844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f1606899-da17-471c-aaf1-47f5ab2a69b5 · outbound

This paper cites Causaltime: Realistically generated time-series for benchmarking of causal discov- ery,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Causaltime: Realistically generated time-series for benchmarking of causal discov- ery,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:50.014412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aebb11e1-e770-418c-aaeb-a6c09bfcfa93 · outbound

This paper cites Jacobian regularizer- based neural granger causality,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Jacobian regularizer- based neural granger causality,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:49.884445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 991a2843-daba-4ae3-8daf-a359fa8a443e · outbound

This paper cites Base control for the tennessee eastman problem,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Base control for the tennessee eastman problem,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:49.755403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 05651d86-f0d2-4429-9d62-d16102b7d171 · outbound

This paper cites Cipcad-bench: Continuous industrial process datasets for benchmarking causal discovery methods,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Cipcad-bench: Continuous industrial process datasets for benchmarking causal discovery methods,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:49.620802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cf1165d6-a660-4ca6-8d24-89da741da345 · outbound

This paper cites Soft sensors for product quality monitoring in debutanizer distillation columns,.

A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes Soft sensors for product quality monitoring in debutanizer distillation columns,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:23:49.483396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation f4b4a320-8f48-4c8c-890a-3f6620ea545d · inbound

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis cites this paper.

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes

Reference 14

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T15:18:53.132218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.