Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1912.03277.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T20:30:31.050416Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T20:48:55.170341Z
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 a3013fb3-97d2-469c-85f2-94cdac4851b8 · inbound
RealAC: A Domain-Agnostic Framework for Realistic and Actionable Counterfactual Explanations cites this paper.
RealAC: A Domain-Agnostic Framework for Realistic and Actionable Counterfactual Explanations Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T20:30:31.050416Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9fb60697-4c1d-48ea-92b2-90ff29f0f952 · inbound
An Explainable Gaussian Process Auto-encoder for Tabular Data cites this paper.
An Explainable Gaussian Process Auto-encoder for Tabular Data Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T13:13:50.613029Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b2343861-4d0c-4f8e-97cb-4442870547bc · inbound
From Universal to Individualized Actionability: Revisiting Personalization in Algorithmic Recourse cites this paper.
From Universal to Individualized Actionability: Revisiting Personalization in Algorithmic Recourse Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
Reference 32
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T06:15:56.450345Z
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 9559f24c-802a-4fcb-9ac5-d72658343e26 · inbound
Causal Algorithmic Recourse: Foundations and Methods cites this paper.
Causal Algorithmic Recourse: Foundations and Methods Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T02:32:06.112645Z
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 5cf0bdcc-9459-4ff2-a085-384f514e5a70 · inbound
PACE: A Neuro-Symbolic Framework for Plausible and Actionable Counterfactual Explanations cites this paper.
PACE: A Neuro-Symbolic Framework for Plausible and Actionable Counterfactual Explanations Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T20:48:55.172910Z
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.