Paper Citation Record · LEDGER

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery

As of 7 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2608.04930.

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

pith.paper-citation-record.v1
2608.04930 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T13:39:09.048900Z

measured 18 of 18 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

18 of 18 outbound references displayed

verified exact1
verified fuzzy4
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0c40e58a-aa01-4e65-b9de-b043fb75e59b · outbound

This paper cites Ranking via Sinkhorn Propagation.

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery Ranking via Sinkhorn Propagation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:07.309268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ad28293-7872-48ae-937f-58adf1570e62 · outbound

This paper cites A Meta-Learning Approach to Bayesian Causal Discovery.

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery A Meta-Learning Approach to Bayesian Causal Discovery

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:07.683181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2effe7b-a811-4914-abeb-663692284649 · outbound

This paper cites The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables.

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:08.095719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74ebf365-1e4a-4ee3-a2c0-7ad569ed61cb · outbound

This paper cites A Graph Autoencoder Approach to Causal Structure Learning.

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery A Graph Autoencoder Approach to Causal Structure Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:08.210176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bfe1161-0b07-4e5a-9ad0-cc48389efc3c · outbound

This paper cites Masked gradient-based causal structure learning.

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery Masked gradient-based causal structure learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:11.024934Z

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 c2c775dc-d482-4f9d-b0ed-e2b40ad1f5c0 · outbound

This paper cites Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes.

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:08.442575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b823d65-1ff9-4919-bd74-8206e91853d6 · outbound

This paper cites Advances in variational inference.IEEE transactions on pattern analysis and machine intelligence, 41(8):2008–2026,.

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery Advances in variational inference.IEEE transactions on pattern analysis and machine intelligence, 41(8):2008–2026,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:08.724493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6de43ac7-9ea2-4b56-97f0-dae426bd0cc4 · outbound

This paper cites Thus the marginal law ofB ν isBernoulli(E[π ν])and converges toBernoulli(p).

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery Thus the marginal law ofB ν isBernoulli(E[π ν])and converges toBernoulli(p)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:10.085299Z

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 6a0e667b-6700-46e7-a973-7155f73e801b · outbound

This paper cites Causal discovery with continuous additive noise models.The Journal of Machine Learning Research, 15(1):2009–2053,.

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery Causal discovery with continuous additive noise models.The Journal of Machine Learning Research, 15(1):2009–2053,

Reference 2009

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:10.526666Z

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 e9835329-b946-46b7-92c9-b9ce2cf409ce · outbound

This paper cites Gradient-Based Neural DAG Learning.

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery Gradient-Based Neural DAG Learning

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:07.946473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 005506b3-5896-45fd-9886-e663456f3cbb · outbound

This paper cites Adam: A Method for Stochastic Optimization.

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery Adam: A Method for Stochastic Optimization

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:07.875483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20dda301-eba7-4f27-89c1-f0b18f7091be · outbound

This paper cites Differentiable constraint-based causal discovery.arXiv preprint arXiv:2510.22031,.

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery Differentiable constraint-based causal discovery.arXiv preprint arXiv:2510.22031,

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:08.816834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 646f699a-8cd1-43a1-874c-051d989ff915 · outbound

This paper cites The most computationally intensive components are the formation of the relaxed acyclicity mask and particle interactions in the SVGD style update.

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery The most computationally intensive components are the formation of the relaxed acyclicity mask and particle interactions in the SVGD style update

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:10.291634Z

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 01b87d93-c0cf-494e-a15f-65b55b385066 · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery Categorical Reparameterization with Gumbel-Softmax

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:07.787709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70ce73f8-6938-4401-ae65-608624abacae · outbound

This paper cites Prodag: Projected variational inference for directed acyclic graphs.arXiv preprint arXiv:2405.15167,.

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery Prodag: Projected variational inference for directed acyclic graphs.arXiv preprint arXiv:2405.15167,

Reference 2021

Resolution: verified exact; raw_fallback, observed 2026-08-06T13:39:09.567134Z

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 eaca4102-ad8f-4cbd-a37d-1ca50780fb0e · outbound

This paper cites Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?.

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:07.458067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6486eb8-3549-4bb4-a24c-981d29ba552f · outbound

This paper cites Variational Causal Networks: Approximate Bayesian Inference over Causal Structures.

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery Variational Causal Networks: Approximate Bayesian Inference over Causal Structures

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:07.357579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60bfcc63-1db7-4be9-a799-19dbb06201f7 · outbound

This paper cites International ai safety report 2026.arXiv preprint arXiv:2602.21012,.

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery International ai safety report 2026.arXiv preprint arXiv:2602.21012,

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:07.551211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.