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Paper Citation Record · LEDGER

MDL Meets Latent Confounders: LNML-based Causal Discovery

As of 13 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2607.04133.

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

pith.paper-citation-record.v1
2607.04133 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T21:27:21.928806Z

measured 28 of 28 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

28 of 28 outbound references displayed

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Outbound references

Observation 67d6440f-b7ff-4e4b-8802-47c5bfd040f5 · outbound

This paper cites Knowledge and Information Systems56(2), 285–307 (2018) 3 https://archive.ics.uci.edu/dataset/9/auto+mpg MDL Meets Latent Confounders: LNML-based Causal Discovery 17.

MDL Meets Latent Confounders: LNML-based Causal Discovery Knowledge and Information Systems56(2), 285–307 (2018) 3 https://archive.ics.uci.edu/dataset/9/auto+mpg MDL Meets Latent Confounders: LNML-based Causal Discovery 17

Reference 1

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Observation 3fba1948-4498-4d01-bbe3-303a40b21bef · outbound

This paper cites Journal of machine learning research3(Nov), 507–554 (2002).

MDL Meets Latent Confounders: LNML-based Causal Discovery Journal of machine learning research3(Nov), 507–554 (2002)

Reference 2

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Observation b1ad9afc-5639-4945-9492-80f616eec8de · outbound

This paper cites an unresolved cited work.

MDL Meets Latent Confounders: LNML-based Causal Discovery Unresolved cited work

Reference 3

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Observation 20591aba-6808-45fd-80a8-1cf89c82a921 · outbound

This paper cites an unresolved cited work.

MDL Meets Latent Confounders: LNML-based Causal Discovery Unresolved cited work

Reference 4

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Observation 26d884a0-ff3e-4d37-96e3-70d13814565f · outbound

This paper cites Advances in neural information processing systems21(2008).

MDL Meets Latent Confounders: LNML-based Causal Discovery Advances in neural information processing systems21(2008)

Reference 5

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Observation 77540717-c655-4063-9f26-358c1cf97815 · outbound

This paper cites Advances in neural information processing systems17(2004).

MDL Meets Latent Confounders: LNML-based Causal Discovery Advances in neural information processing systems17(2004)

Reference 6

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Observation 2b1a7b4b-99ce-46b5-bdcc-a3ce6f751cd0 · outbound

This paper cites Advances in Neural Information Processing Systems18(2005).

MDL Meets Latent Confounders: LNML-based Causal Discovery Advances in Neural Information Processing Systems18(2005)

Reference 7

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Observation f697b7d9-ac44-4c32-9750-6d9f1b916bf8 · outbound

This paper cites In: Proceedings of the 2019 SIAM International Conference on Data Mining.

MDL Meets Latent Confounders: LNML-based Causal Discovery In: Proceedings of the 2019 SIAM International Conference on Data Mining

Reference 8

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Observation fc84eb86-9508-43e0-8090-f8573f9c1d2d · outbound

This paper cites In: Uncertainty in Artificial Intelligence.

MDL Meets Latent Confounders: LNML-based Causal Discovery In: Uncertainty in Artificial Intelligence

Reference 9

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Observation c1b0bfc5-197c-4b93-8c09-fcbf17eee171 · outbound

This paper cites In: Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S., Scarlett, J.

MDL Meets Latent Confounders: LNML-based Causal Discovery In: Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S., Scarlett, J

Reference 10

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Observation 85ca214f-ed8c-4d28-97b8-204550b29707 · outbound

This paper cites In: 2022 IEEE International Conference on Big Data.

MDL Meets Latent Confounders: LNML-based Causal Discovery In: 2022 IEEE International Conference on Big Data

Reference 11

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Observation 45d3b7f7-459b-4cf4-b212-8f6e56b30cb2 · outbound

This paper cites In: International Conference on artificial intelligence and statistics.

MDL Meets Latent Confounders: LNML-based Causal Discovery In: International Conference on artificial intelligence and statistics

Reference 12

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Observation 9a547246-7cb3-4c0e-98fe-9dca53f8e469 · outbound

This paper cites In: 2017 IEEE international conference on data mining.

MDL Meets Latent Confounders: LNML-based Causal Discovery In: 2017 IEEE international conference on data mining

Reference 13

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Observation 9286f630-2d90-4d3d-81cb-2a84c8f556c3 · outbound

This paper cites In: Proceedings of the AAAI Conference on Artificial Intelligence.

MDL Meets Latent Confounders: LNML-based Causal Discovery In: Proceedings of the AAAI Conference on Artificial Intelligence

Reference 14

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Observation a298d2fc-ff8d-4e94-9516-1a18d7c656a3 · outbound

This paper cites Normalized Maximum Likelihood with Luckiness for Multivariate Normal Distributions.

MDL Meets Latent Confounders: LNML-based Causal Discovery Normalized Maximum Likelihood with Luckiness for Multivariate Normal Distributions

Reference 15

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Observation 9f6e37f1-eb2f-41e9-a630-5a6562957bc4 · outbound

This paper cites Cambridge University Press, Cambridge, UK, 1st edn.

MDL Meets Latent Confounders: LNML-based Causal Discovery Cambridge University Press, Cambridge, UK, 1st edn

Reference 16

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Observation 9063c4b7-89ea-48b2-be1e-d153effc5f41 · outbound

This paper cites Neural computation27(3), 771–799 (2015).

MDL Meets Latent Confounders: LNML-based Causal Discovery Neural computation27(3), 771–799 (2015)

Reference 17

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Observation 4d7b4b17-dcfc-41a2-a3c1-bce11fcba1fd · outbound

This paper cites International journal of data science and analytics3, 121–129 (2017).

MDL Meets Latent Confounders: LNML-based Causal Discovery International journal of data science and analytics3, 121–129 (2017)

Reference 18

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Observation 4ca9fb9a-9ea8-4ba1-921b-d529198f0ec1 · outbound

This paper cites MIT Press, Cambridge, MA (2006),http://www.gaussianprocess.org/gpml/ 18 Z.

MDL Meets Latent Confounders: LNML-based Causal Discovery MIT Press, Cambridge, MA (2006),http://www.gaussianprocess.org/gpml/ 18 Z

Reference 19

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Observation d85f4750-26c8-4c22-8e4d-6e85cb5dce05 · outbound

This paper cites Automatica14(5), 465–471 (1978).

MDL Meets Latent Confounders: LNML-based Causal Discovery Automatica14(5), 465–471 (1978)

Reference 20

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Observation 7f2efafc-f518-47f3-ba52-f14cd01043bd · outbound

This paper cites Journal of Machine Learning Research 7(10) (2006).

MDL Meets Latent Confounders: LNML-based Causal Discovery Journal of Machine Learning Research 7(10) (2006)

Reference 21

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Observation 9246064c-d166-4085-89d8-87e976909c6a · outbound

This paper cites Journal of Machine Learning Research12(Apr), 1225–1248 (2011).

MDL Meets Latent Confounders: LNML-based Causal Discovery Journal of Machine Learning Research12(Apr), 1225–1248 (2011)

Reference 22

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Observation a1c9522c-4919-4d0f-a554-90644355c208 · outbound

This paper cites Problemy Peredachi Informatsii23(3), 3–17 (1987).

MDL Meets Latent Confounders: LNML-based Causal Discovery Problemy Peredachi Informatsii23(3), 3–17 (1987)

Reference 23

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Observation 7137309c-4471-4bb8-ab4f-b3c34af2ef95 · outbound

This paper cites MIT Press, Cambridge, MA, 2nd edn.

MDL Meets Latent Confounders: LNML-based Causal Discovery MIT Press, Cambridge, MA, 2nd edn

Reference 24

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Observation 1a6e3f98-38fd-4b5b-a8a4-e2d3ccd2d1bb · outbound

This paper cites Neural computation26(1), 57–83 (2014).

MDL Meets Latent Confounders: LNML-based Causal Discovery Neural computation26(1), 57–83 (2014)

Reference 25

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Observation 7ec20203-f761-45dc-995b-dd41b9cd3a91 · outbound

This paper cites Springer Nature, Berlin, Heidelberg (2023).

MDL Meets Latent Confounders: LNML-based Causal Discovery Springer Nature, Berlin, Heidelberg (2023)

Reference 26

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Observation 896b6e1e-90cb-46f6-8302-d1e7880c5747 · outbound

This paper cites Data Mining and Knowledge Discovery33(4), 1017–1058 (2019).

MDL Meets Latent Confounders: LNML-based Causal Discovery Data Mining and Knowledge Discovery33(4), 1017–1058 (2019)

Reference 27

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Observation 8a4a50ae-cb79-414e-af65-77a9e2647d12 · outbound

This paper cites In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intel- ligence.

MDL Meets Latent Confounders: LNML-based Causal Discovery In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intel- ligence

Reference 28

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Pith citing papers

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