Paper Citation Record · LEDGER

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms

As of 15 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 1 inbound Pith citation observation for arXiv:2412.10039.

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

pith.paper-citation-record.v1
2412.10039 v2

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T16:33:31.416678Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T16:26:27.430214Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-08T16:26:27.703130Z

Reference resolution

30 of 30 outbound references displayed

verified exact1
verified fuzzy18
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fe601a61-5e11-45e6-b309-d833e947f420 · outbound

This paper cites Comparison of statistical methods for finding network motifs.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Comparison of statistical methods for finding network motifs

Reference 1

Resolution: verified exact; doi, observed 2026-08-11T16:33:31.547621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 73db47d9-f913-4ced-9cc4-046b3e0fc8a3 · outbound

This paper cites Learning high-dimensional directed acyclic graphs with mixed data-types.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Learning high-dimensional directed acyclic graphs with mixed data-types

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:33:32.816923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 751ef67b-0e77-4585-9cbb-e48f24b77969 · outbound

This paper cites Fast scalable and accurate discovery of dags using the best order score search and grow shrink trees.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Fast scalable and accurate discovery of dags using the best order score search and grow shrink trees

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:33:32.645978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f5e658b4-b3a2-4a2d-9c9f-bc17d3107481 · outbound

This paper cites Improving Finite Sample Performance of Causal Discovery by Exploiting Temporal Structure.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Improving Finite Sample Performance of Causal Discovery by Exploiting Temporal Structure

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T16:33:30.965374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54b2564f-327c-42d5-aeb3-a8ccadae4517 · outbound

This paper cites Tuning causal discovery algorithms.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Tuning causal discovery algorithms

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:33:32.597545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 08814538-dc5d-4540-bcb2-f2098694a6fe · outbound

This paper cites Optimal structure identification with greedy search.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Optimal structure identification with greedy search

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T16:33:30.976249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6eefeffa-d111-4ed1-b186-df383bc8373c · outbound

This paper cites Toward Falsifying Causal Graphs Using a Permutation-Based Test.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Toward Falsifying Causal Graphs Using a Permutation-Based Test

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T16:33:30.980277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8549fee6-b311-4431-be65-42cf9acbe03e · outbound

This paper cites The case for evaluating causal models using interventional measures and empirical data.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms The case for evaluating causal models using interventional measures and empirical data

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:33:32.577710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ac5cdece-6d67-459d-bb6a-843f59a2738e · outbound

This paper cites Can algorithms replace expert knowledge for causal inference? a case study on novice use of causal discovery.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Can algorithms replace expert knowledge for causal inference? a case study on novice use of causal discovery

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:33:32.563997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5a8929c8-e81f-462d-8d45-3b1248bab1be · outbound

This paper cites Adjustment identification distance: A gadjid for causal structure learning.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Adjustment identification distance: A gadjid for causal structure learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:33:32.495656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b43ddc80-2f01-4bb8-aa45-301d58d25362 · outbound

This paper cites The (mis) use of overlap of confidence intervals to assess effect modification.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms The (mis) use of overlap of confidence intervals to assess effect modification

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:33:32.433149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 56e12bba-1d72-4a15-9845-e02e16c8d5c4 · outbound

This paper cites Gradient-based neural dag learning.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Gradient-based neural dag learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:33:32.420280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 44f18399-c50e-4687-9dea-5f0e47f7d42a · outbound

This paper cites Greedy relaxations of the sparsest permutation algorithm.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Greedy relaxations of the sparsest permutation algorithm

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:33:32.407410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d7977c7c-8b19-47f6-9ecf-197f16810372 · outbound

This paper cites Supervised Whole DAG Causal Discovery.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Supervised Whole DAG Causal Discovery

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T16:33:31.140836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1d4fbb5-e08a-4a53-b808-637d5e390d76 · outbound

This paper cites SID: Structural Intervention Distance, 2023.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms SID: Structural Intervention Distance, 2023

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:33:32.310289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f284d773-6b23-4211-9380-203283910aca · outbound

This paper cites Structural intervention distance for evaluating causal graphs.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Structural intervention distance for evaluating causal graphs

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T16:33:31.148925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b35d1d8-81d5-463e-aab9-f79d435a0368 · outbound

This paper cites Data-driven model building for life-course epidemiology.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Data-driven model building for life-course epidemiology

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:33:32.220992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ab7dd7ac-d9a6-4d1d-b1ea-5450873619e5 · outbound

This paper cites causalDisco: Tools for Causal Discovery on Observational Data, 2022.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms causalDisco: Tools for Causal Discovery on Observational Data, 2022

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:33:32.208266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 67e76126-9913-47e1-bbf2-a40ec7ad9ae3 · outbound

This paper cites Constructing causal life-course models: Comparative study of data-driven and theory-driven approaches.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Constructing causal life-course models: Comparative study of data-driven and theory-driven approaches

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:33:32.194549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0fbcac51-11c7-4943-b693-d392ed0d6171 · outbound

This paper cites Causal discovery for observational sciences using supervised machine learning.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Causal discovery for observational sciences using supervised machine learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:33:32.181004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9de5d3ce-e995-40b0-865d-4af83bf77274 · outbound

This paper cites Tetrad—a toolbox for causal discovery.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Tetrad—a toolbox for causal discovery

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:33:31.994204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 51c94605-5d15-4fbb-9c27-091fd3375e13 · outbound

This paper cites Causal protein-signaling networks derived from multiparameter single-cell data.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Causal protein-signaling networks derived from multiparameter single-cell data

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T16:33:31.175286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89643f5d-4e33-4682-9652-9e75c0f60fce · outbound

This paper cites A linear non-gaussian acyclic model for causal discovery.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms A linear non-gaussian acyclic model for causal discovery

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T16:33:31.226207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation baef92f3-0a86-487a-9553-fba6b3c8c983 · outbound

This paper cites An algorithm for fast recovery of sparse causal graphs.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms An algorithm for fast recovery of sparse causal graphs

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T16:33:31.305533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34afdc1a-abf2-4cd4-af1f-8d7dbbc39b19 · outbound

This paper cites Causation, prediction, and search.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Causation, prediction, and search

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T16:33:31.395521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e69bcacb-85ae-4484-a95f-b6587873cb4c · outbound

This paper cites The max-min hill-climbing bayesian network structure learning algorithm.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms The max-min hill-climbing bayesian network structure learning algorithm

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T16:33:31.399236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc21edcc-e96d-4f07-b184-26fc24a4e38a · outbound

This paper cites Separation-based distance measures for causal graphs.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Separation-based distance measures for causal graphs

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:33:31.868768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation aa7576a3-ed6d-484d-ae56-6435c356c820 · outbound

This paper cites Causal structure learning with one-dimensional convolutional neural networks.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Causal structure learning with one-dimensional convolutional neural networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:33:31.855929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c3720b27-6984-48a5-be34-d40af180deb8 · outbound

This paper cites Dag-gnn: Dag structure learning with graph neural networks.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Dag-gnn: Dag structure learning with graph neural networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:33:31.783482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b4e200b5-5bef-48bd-a4e6-0516dd94d65b · outbound

This paper cites Dags with no tears: Continuous optimization for structure learning.

Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms Dags with no tears: Continuous optimization for structure learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T16:33:31.416678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation bc7026c1-67ad-47e0-b776-78f0aba2f149 · inbound

Score-Based Causal Discovery with Temporal Background Information cites this paper.

Score-Based Causal Discovery with Temporal Background Information Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-08T16:26:27.707247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.