Paper Citation Record · LEDGER

Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:1912.03277.

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

pith.paper-citation-record.v1
1912.03277 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:43:06.675767Z

measured 0 of 1 external citation measurements

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 7a0bccc0-2573-4e83-8029-93135ad4802e · inbound

Optimal probabilistic feature shifts for reclassification in tree ensembles cites this paper.

Optimal probabilistic feature shifts for reclassification in tree ensembles Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T22:17:35.674341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7035ebf1-5ffe-4992-b97c-f83d9f06df88 · inbound

Graph Counterfactual Explainable AI via Latent Space Traversal cites this paper.

Graph Counterfactual Explainable AI via Latent Space Traversal Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T20:20:17.989479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94294f8d-e34d-4879-80f0-a9fb3f24067f · inbound

On Measuring Intrinsic Causal Attributions in Deep Neural Networks cites this paper.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:06.675767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+00:00.