Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T13:55:49.990484Z
Paper Citation Record · LEDGER
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series
As of 9 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2505.20697.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
46 of 46 outbound references displayed
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- verified fuzzy37
- unresolved7
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 5a50dfdb-bdb7-4cb7-830b-52a8792e2110 · outbound
This paper cites write newline.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series write newline
Reference 1
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 65018d9b-a5ff-4863-be85-88e0f80d02c0 · outbound
This paper cites A., Liu, J., Kiehl, K.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series A., Liu, J., Kiehl, K
Reference 2
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 97a5a578-4a8e-4ee0-9ae9-1463f46ed30d · outbound
This paper cites K., Devijver, E., and Gaussier, E.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series K., Devijver, E., and Gaussier, E
Reference 3
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 130747a1-bd64-4124-ad8f-6fca23ec4911 · outbound
This paper cites Causal discovery from conditionally stationary time series.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Causal discovery from conditionally stationary time series
Reference 4
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation d520bbd9-39f0-4624-9b52-6ab80fd685ec · outbound
This paper cites Neural additive vector autoregression models for causal discovery in time series.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Neural additive vector autoregression models for causal discovery in time series
Reference 5
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation baa8d2e8-fefa-4c92-b47a-1b7c2c524172 · outbound
This paper cites D., Miller, R., Pearlson, G., and Adal , T.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series D., Miller, R., Pearlson, G., and Adal , T
Reference 6
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation d12e53c0-4a3d-43e3-900c-5a93b0714132 · outbound
This paper cites Multi-region local field potential recordings during a tail-suspension test.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Multi-region local field potential recordings during a tail-suspension test
Reference 7
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 136bbbe5-ae3f-4e64-a324-4103a0f986d0 · outbound
This paper cites Dycast: learning dynamic causal structure from time series.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Dycast: learning dynamic causal structure from time series
Reference 8
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 1ae0e29d-1b24-4fbf-b046-4b07e93573ac · outbound
This paper cites Multiscale Causal Structure Learning.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Multiscale Causal Structure Learning
Reference 9
- Resolution
- verified exact
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 73020cde-1725-450b-a7ca-bf860c4ebd29 · outbound
This paper cites M., Kumar, S., Dancy, E.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series M., Kumar, S., Dancy, E
Reference 10
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 7bb1dc78-00cf-4aed-a59e-6438a29e754e · outbound
This paper cites B., Jordan, M.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series B., Jordan, M
Reference 11
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 1fbfb0cd-c990-43fd-b311-aab851aa50a1 · outbound
This paper cites an unresolved cited work.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Unresolved cited work
Reference 12
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation c3580c61-550d-4dc2-b941-e4c9c2d3167d · outbound
This paper cites J., Harrison, L., and Penny, W.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series J., Harrison, L., and Penny, W
Reference 13
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation c8e35ab0-23e9-485b-a50b-8c6173df7b03 · outbound
This paper cites Causal discovery for non-stationary non-linear time series data using just-in-time modeling.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Causal discovery for non-stationary non-linear time series data using just-in-time modeling
Reference 14
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation a2602164-0416-4fbe-9dff-f09aece8982a · outbound
This paper cites Directed spectrum measures improve latent network models of neural populations.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Directed spectrum measures improve latent network models of neural populations
Reference 15
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 048d2874-1709-410e-9d7f-8b122a7bc4c4 · outbound
This paper cites and Runge, J.
Reference 16
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 324d2fd5-333f-4556-82de-19995ab13efc · outbound
This paper cites Switching neural network systems for nonlinear tracking.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Switching neural network systems for nonlinear tracking
Reference 17
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation fa1e0069-6983-4014-b6c4-45034dfedc1a · outbound
This paper cites Adjustment identification distance: A gadjid for causal structure learning.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Adjustment identification distance: A gadjid for causal structure learning
Reference 18
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation f5e6be75-eaa5-4b18-806e-6edee608ca4f · outbound
This paper cites Causal discovery and forecasting in nonstationary environments with state-space models.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Causal discovery and forecasting in nonstationary environments with state-space models
Reference 19
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation b34a0739-8270-4a9e-be3e-fa531c7ca91c · outbound
This paper cites R., Sedler, A.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series R., Sedler, A
Reference 20
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 07ee5aff-7cb0-4c16-8c67-6c98eecf0544 · outbound
This paper cites an unresolved cited work.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Unresolved cited work
Reference 21
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 4a19dedc-ca0c-4fec-99d3-d07e7702b24c · outbound
This paper cites Exploring and making sense of large graphs.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Exploring and making sense of large graphs
Reference 22
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 5844059b-c05c-4cdb-af77-22f88fd220c5 · outbound
This paper cites State space reconstruction parameters in the analysis of chaotic time series—the role of the time window length.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series State space reconstruction parameters in the analysis of chaotic time series—the role of the time window length
Reference 23
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation deee94d3-892f-4610-8737-4a5b94565560 · outbound
This paper cites Bayesian learning and inference in recurrent switching linear dynamical systems.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Bayesian learning and inference in recurrent switching linear dynamical systems
Reference 24
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 23f137b7-9824-4a42-a12b-23fcccdd8b30 · outbound
This paper cites Amortized causal discovery: Learning to infer causal graphs from time-series data.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Amortized causal discovery: Learning to infer causal graphs from time-series data
Reference 25
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 16f17f99-dbdb-4e4a-9d08-1a66e3143ce7 · outbound
This paper cites What can be estimated? Identifiability, estimability, causal inference and ill-posed inverse problems.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series What can be estimated? Identifiability, estimability, causal inference and ill-posed inverse problems
Reference 26
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4ec71593-a3e6-44cc-bffe-15badc2fd8f8 · outbound
This paper cites D., Talbot, A., Blount, C., Walder-Christensen, K.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series D., Talbot, A., Blount, C., Walder-Christensen, K
Reference 27
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 6e778570-af90-40a4-9a3a-32048681e1f6 · outbound
This paper cites Generating realistic in silico gene networks for performance assessment of reverse engineering methods.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Generating realistic in silico gene networks for performance assessment of reverse engineering methods
Reference 28
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation b1ad43d9-f0df-4fc5-a72c-3cf5fc001138 · outbound
This paper cites Dynotears: Structure learning from time-series data.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Dynotears: Structure learning from time-series data
Reference 29
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 025f4644-f475-47d7-96db-6442c219d9db · outbound
This paper cites Causality.
Reference 30
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 26bc062e-5257-4e25-bb59-49dc33b1807d · outbound
This paper cites Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets
Reference 31
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 0bc95b95-0d37-40ea-941a-1a8884effafd · outbound
This paper cites Causal discovery in financial markets: A framework for nonstationary time-series data, 2024.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Causal discovery in financial markets: A framework for nonstationary time-series data, 2024
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:55:52.709119Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation f1d2bbf9-4e80-4bd5-b0d3-9e537948e4d9 · outbound
This paper cites Reconstructing regime-dependent causal relationships from observational time series.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Reconstructing regime-dependent causal relationships from observational time series
Reference 33
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 3e51860a-a8b9-4cd5-a228-02b8622171f7 · outbound
This paper cites Granger causality.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Granger causality
Reference 34
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 185d6b3f-10fd-4215-9570-c69bfaa85c3e · outbound
This paper cites and Fox, E.
Reference 35
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 46fbb8f5-3d3a-4647-b6a5-bc94e559a0e4 · outbound
This paper cites Eeg emotion recognition using dynamical graph convolutional neural networks.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Eeg emotion recognition using dynamical graph convolutional neural networks
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:55:51.969692Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 5423cce1-0ddb-4439-9fe9-50a4eaa85005 · outbound
This paper cites E., Penny, W.
Reference 37
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation b07b3bcc-a24d-446f-ab1f-ccbdf2db9481 · outbound
This paper cites Estimating a brain network predictive of stress and genotype with supervised autoencoders.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Estimating a brain network predictive of stress and genotype with supervised autoencoders
Reference 38
- Resolution
- verified exact
- doi, observed 2026-08-07T13:55:50.187742Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 07e1cba6-ee68-4d62-9d5b-d801120f6ba0 · outbound
This paper cites Anxiety control by astrocytes in the lateral habenula.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Anxiety control by astrocytes in the lateral habenula
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:55:51.640178Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 14425d2d-7894-44f9-8066-49bb06f80bd5 · outbound
This paper cites an unresolved cited work.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Unresolved cited work
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:55:49.505617Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6bace8cd-a563-4843-b43b-c7f4d88d49f7 · outbound
This paper cites E., Mogensen, P.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series E., Mogensen, P
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:55:51.502528Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation fe91a8db-4e16-452c-b779-384c7ecce5bc · outbound
This paper cites Dag-gnn: Dag structure learning with graph neural networks.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Dag-gnn: Dag structure learning with graph neural networks
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:55:51.360519Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation dcb0cbb5-6a4a-44cb-9756-126d3e8b7f43 · outbound
This paper cites Causal discovery from nonstationary/heterogeneous data: Skeleton estimation and orientation determination.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Causal discovery from nonstationary/heterogeneous data: Skeleton estimation and orientation determination
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:55:51.238349Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 44589e51-b889-4b85-b4af-0e57c68039d5 · outbound
This paper cites Causal discovery from nonstationary/heterogeneous data: Skeleton estimation and orientation determination.
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Causal discovery from nonstationary/heterogeneous data: Skeleton estimation and orientation determination
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:55:51.118101Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 82cb9cde-6ef9-4490-b0d6-5c42bd35e6c3 · outbound
This paper cites and Wang, J.
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:55:50.977663Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 368a47a2-04a1-4511-a898-29fed8096d4b · outbound
This paper cites and Feng, J.
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:55:50.632019Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Pith citing papers
No inbound Pith citation observations are available.