Pith. sign in

Paper Citation Record · LEDGER

Interpretable Neural Causal Models with TRAM-DAGs

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2503.16206.

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

pith.paper-citation-record.v1
2503.16206 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:58:52.047463Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T18:30:14.837638Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c1a5435c-6665-48f4-9254-3aa6808cadd2 · inbound

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios cites this paper.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Interpretable Neural Causal Models with TRAM-DAGs

Reference 82

Resolution
unresolved
no resolver link, observed 2026-08-06T22:58:52.047463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:58:52.047463Z digest=sha256:ad5aa8cb88f7605995f5027b41845cf5afd74a6fbdb0702212bf072c051d547f

Observation 5f1aa734-c594-41bb-9dd2-1f516efe43e0 · inbound

Rethinking Spatio-Temporal Anomaly Detection: A Vision for Causality-Driven Cybersecurity cites this paper.

Rethinking Spatio-Temporal Anomaly Detection: A Vision for Causality-Driven Cybersecurity Interpretable Neural Causal Models with TRAM-DAGs

Reference 28

Resolution
verified exact
local_arxiv, observed 2026-08-06T18:30:14.842173Z

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.

source=pdf_text observed=2026-08-06T18:30:14.432978Z digest=sha256:d241dae0a611e4553a4c7cda0be0f84537f9d4e9eb6d89c0c4086a88d5b0caa2