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Paper Citation Record · LEDGER

Finite sample-optimal adjustment sets in linear Gaussian causal models

As of 9 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2506.12869.

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

pith.paper-citation-record.v1
2506.12869 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:44:46.070696Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

23 of 23 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 10a3a6b8-9966-44ea-9852-eb0f47c22fbd · outbound

This paper cites an unresolved cited work.

Finite sample-optimal adjustment sets in linear Gaussian causal models Unresolved cited work

Reference 1

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This paper cites Variable selection for propensity score models.

Finite sample-optimal adjustment sets in linear Gaussian causal models Variable selection for propensity score models

Reference 2

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Observation 7e4ee277-7586-4cee-acb4-f5e577a03f25 · outbound

This paper cites Long story short: Omitted variable bias in causal machine learning.

Finite sample-optimal adjustment sets in linear Gaussian causal models Long story short: Omitted variable bias in causal machine learning

Reference 3

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Observation 8f635d41-5d3a-4cfe-bf96-828344b3d585 · outbound

This paper cites Making Sense of Sensitivity: Extending Omitted Variable Bias.

Finite sample-optimal adjustment sets in linear Gaussian causal models Making Sense of Sensitivity: Extending Omitted Variable Bias

Reference 4

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Observation d84d80c0-54d4-43b7-8f5c-94d6df1d00d2 · outbound

This paper cites an unresolved cited work.

Finite sample-optimal adjustment sets in linear Gaussian causal models Unresolved cited work

Reference 5

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This paper cites Markov Properties for Graphical Models with Cycles and Latent Variables.

Finite sample-optimal adjustment sets in linear Gaussian causal models Markov Properties for Graphical Models with Cycles and Latent Variables

Reference 6

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This paper cites an unresolved cited work.

Finite sample-optimal adjustment sets in linear Gaussian causal models Unresolved cited work

Reference 7

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Observation ba504ae9-7933-4aa3-aadc-8aef8ebb6049 · outbound

This paper cites Richard Guo, Emilija Perkovi \'c , and Andrea Rotnitzky.

Finite sample-optimal adjustment sets in linear Gaussian causal models Richard Guo, Emilija Perkovi \'c , and Andrea Rotnitzky

Reference 8

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Source-reported events for the cited work

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Observation 621c5e20-b68d-47db-9264-dd6b30cf33aa · outbound

This paper cites Maathuis.

Finite sample-optimal adjustment sets in linear Gaussian causal models Maathuis

Reference 9

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Source-reported events for the cited work

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Observation bd30b4bb-bd68-4e07-8c66-b79af146253f · outbound

This paper cites Hernan and J.M.

Finite sample-optimal adjustment sets in linear Gaussian causal models Hernan and J.M

Reference 10

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Observation 7f1d3d26-4e31-4e69-bb1a-5f6f5a8ea5d8 · outbound

This paper cites Middleton, Marc A.

Finite sample-optimal adjustment sets in linear Gaussian causal models Middleton, Marc A

Reference 11

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Source-reported events for the cited work

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Observation ae419c7b-3e9c-4b97-97cb-8c2c1e135453 · outbound

This paper cites Myers, Jeremy A.

Finite sample-optimal adjustment sets in linear Gaussian causal models Myers, Jeremy A

Reference 12

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Observation f73428ea-3d0c-4a68-b059-5d480bf2722f · outbound

This paper cites Factorization of the partial covariance in singly-connected path diagrams.

Finite sample-optimal adjustment sets in linear Gaussian causal models Factorization of the partial covariance in singly-connected path diagrams

Reference 13

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verified fuzzy
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This paper cites Comment: Graphical models, causality and intervention.

Finite sample-optimal adjustment sets in linear Gaussian causal models Comment: Graphical models, causality and intervention

Reference 14

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Observation f0e6e8de-21fd-4b73-bf66-af5fcde0e14a · outbound

This paper cites Causality: Models, Reasoning and Inference.

Finite sample-optimal adjustment sets in linear Gaussian causal models Causality: Models, Reasoning and Inference

Reference 15

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Source-reported events for the cited work

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This paper cites On a class of bias-amplifying variables that endanger effect estimates.

Finite sample-optimal adjustment sets in linear Gaussian causal models On a class of bias-amplifying variables that endanger effect estimates

Reference 16

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Finite sample-optimal adjustment sets in linear Gaussian causal models Maathuis

Reference 17

Resolution
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Source-reported events for the cited work

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This paper cites Complete graphical characterization and construction of adjustment sets in markov equivalence classes of ancestral graphs.

Finite sample-optimal adjustment sets in linear Gaussian causal models Complete graphical characterization and construction of adjustment sets in markov equivalence classes of ancestral graphs

Reference 18

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Observation 3cd56617-09f8-4b49-8fb5-1e92acc0fc9d · outbound

This paper cites Efficient adjustment sets for population average treatment effect estimation in non-parametric causal graphical models, December 2019.

Finite sample-optimal adjustment sets in linear Gaussian causal models Efficient adjustment sets for population average treatment effect estimation in non-parametric causal graphical models, December 2019

Reference 19

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Finite sample-optimal adjustment sets in linear Gaussian causal models Unresolved cited work

Reference 20

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This paper cites The importance of covariate selection in controlling for selection bias in observational studies.

Finite sample-optimal adjustment sets in linear Gaussian causal models The importance of covariate selection in controlling for selection bias in observational studies

Reference 21

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verified fuzzy
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This paper cites Separators and adjustment sets in causal graphs: Complete criteria and an algorithmic framework.

Finite sample-optimal adjustment sets in linear Gaussian causal models Separators and adjustment sets in causal graphs: Complete criteria and an algorithmic framework

Reference 22

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Source-reported events for the cited work

correction dated 2023-05-25. Source: crossref record 10.1016/j.artint.2023.103938->10.1016/j.artint.2018.12.006:correction, observed 2026-07-11T03:14:13.87948+00:00. This notice travels one citation hop only.

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Observation d6949f7a-ec6b-457a-813d-abfef0c4d7d9 · outbound

This paper cites Wooldridge.

Finite sample-optimal adjustment sets in linear Gaussian causal models Wooldridge

Reference 23

Resolution
verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Pith citing papers

No inbound Pith citation observations are available.