Paper Citation Record · LEDGER

Learning Treatment Representations for Downstream Instrumental Variable Regression

As of 8 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2506.02200.

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

pith.paper-citation-record.v1
2506.02200 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:37:32.747659Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

35 of 35 outbound references displayed

verified exact3
verified fuzzy22
unresolved10
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c75851fb-3b8c-48b1-bf98-77ddab367e1c · outbound

This paper cites Towards efficient representation identification in supervised learning.

Learning Treatment Representations for Downstream Instrumental Variable Regression Towards efficient representation identification in supervised learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:36.463464Z

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Observation 2fdb66bb-ec5d-40ee-9a09-a8588444da3b · outbound

This paper cites Contrastive representations of high-dimensional, structured treatments.

Learning Treatment Representations for Downstream Instrumental Variable Regression Contrastive representations of high-dimensional, structured treatments

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:37:32.950202Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cc7839d5-f96e-4cd1-a645-9b3cdb775918 · outbound

This paper cites Inference on Strongly Identified Functionals of Weakly Identified Functions.

Learning Treatment Representations for Downstream Instrumental Variable Regression Inference on Strongly Identified Functionals of Weakly Identified Functions

Reference 3

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Observation 599c618a-dfbc-408f-8515-a12cdb9acfbe · outbound

This paper cites Source Condition Double Robust Inference on Functionals of Inverse Problems.

Learning Treatment Representations for Downstream Instrumental Variable Regression Source Condition Double Robust Inference on Functionals of Inverse Problems

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T11:37:32.639665Z

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Observation 0b161603-cdbd-4385-b572-9950815b386b · outbound

This paper cites Semiparametric proximal causal inference.

Learning Treatment Representations for Downstream Instrumental Variable Regression Semiparametric proximal causal inference

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:36.325720Z

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Observation b61fd599-71b9-475b-9835-06074d2e6a61 · outbound

This paper cites Capacity management in networks: A structural estimation approach for hospital inpatient wards.

Learning Treatment Representations for Downstream Instrumental Variable Regression Capacity management in networks: A structural estimation approach for hospital inpatient wards

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:36.197367Z

Source-reported events for the cited work

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Observation f7b2180a-e6a9-41a8-b75e-c04cde2ce25d · outbound

This paper cites Off-service placement in inpatient ward network: Resource pooling versus service slowdown.

Learning Treatment Representations for Downstream Instrumental Variable Regression Off-service placement in inpatient ward network: Resource pooling versus service slowdown

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:36.053793Z

Source-reported events for the cited work

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Observation a8a05b46-28ab-4671-a520-08ae81d0634a · outbound

This paper cites Labor-llm: Language-based occupational representations with large language models.

Learning Treatment Representations for Downstream Instrumental Variable Regression Labor-llm: Language-based occupational representations with large language models

Reference 8

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Source-reported events for the cited work

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Observation 7fe28783-087f-40a5-a811-aa9bde4e8100 · outbound

This paper cites On the completeness condition in nonparametric instrumental problems.

Learning Treatment Representations for Downstream Instrumental Variable Regression On the completeness condition in nonparametric instrumental problems

Reference 9

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Observation cc13f727-c7a5-40cd-aafb-3dea2992a12f · outbound

This paper cites A kernel statistical test of independence.

Learning Treatment Representations for Downstream Instrumental Variable Regression A kernel statistical test of independence

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:35.921793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7bbd02be-67d6-436d-81e2-aff2d4d42e01 · outbound

This paper cites Hidden markov nonlinear ica: Unsupervised learning from nonstationary time series.

Learning Treatment Representations for Downstream Instrumental Variable Regression Hidden markov nonlinear ica: Unsupervised learning from nonstationary time series

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:35.727223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 084e8d92-ac2d-46bf-82f8-d87b35e2b8b9 · outbound

This paper cites Identifiable feature learning for spatial data with nonlinear ica.

Learning Treatment Representations for Downstream Instrumental Variable Regression Identifiable feature learning for spatial data with nonlinear ica

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:35.602857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9a3e9b93-1f65-4909-a21d-1d024329e46f · outbound

This paper cites Graphite: Estimating individual effects of graph- structured treatments.

Learning Treatment Representations for Downstream Instrumental Variable Regression Graphite: Estimating individual effects of graph- structured treatments

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:35.448920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3610e14b-f719-43c6-a3a6-b46cba8508ba · outbound

This paper cites Independent component analysis: recent advances.Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371(1984):20110534, 2013.

Learning Treatment Representations for Downstream Instrumental Variable Regression Independent component analysis: recent advances.Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371(1984):20110534, 2013

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:35.258378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4fcafafc-1097-407c-9c3e-eac92c969bf9 · outbound

This paper cites Independent component analysis: algorithms and applications.

Learning Treatment Representations for Downstream Instrumental Variable Regression Independent component analysis: algorithms and applications

Reference 15

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Source-reported events for the cited work

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Observation 116b3d4b-c3db-4e7d-ab70-9b8adea4d840 · outbound

This paper cites Nonlinear independent component analysis for principled disentanglement in unsupervised deep learning.

Learning Treatment Representations for Downstream Instrumental Variable Regression Nonlinear independent component analysis for principled disentanglement in unsupervised deep learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T11:37:32.682214Z

Source-reported events for the cited work

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Observation dd751797-7396-4ab2-960c-bda12518395b · outbound

This paper cites Identifiability of latent-variable and structural-equation models: from linear to nonlinear.

Learning Treatment Representations for Downstream Instrumental Variable Regression Identifiability of latent-variable and structural-equation models: from linear to nonlinear

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:35.078984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation eaca2550-e6ce-4475-b599-82ff76e9486d · outbound

This paper cites Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity.

Learning Treatment Representations for Downstream Instrumental Variable Regression Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T11:37:32.688140Z

Source-reported events for the cited work

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Observation dcf8f555-b204-4f29-85ce-5daa13bfb44a · outbound

This paper cites Instrumented principal component analysis.Available at SSRN 2983919, 2020.

Learning Treatment Representations for Downstream Instrumental Variable Regression Instrumented principal component analysis.Available at SSRN 2983919, 2020

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:34.902329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1cebf07e-113c-40da-a01b-e62e6e07a94c · outbound

This paper cites Variational au- toencoders and nonlinear ica: A unifying framework.

Learning Treatment Representations for Downstream Instrumental Variable Regression Variational au- toencoders and nonlinear ica: A unifying framework

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:34.680027Z

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Observation de698446-8459-4619-a4f9-46ac3412ab3c · outbound

This paper cites Cost-effective incentive allocation via structured counterfactual inference.

Learning Treatment Representations for Downstream Instrumental Variable Regression Cost-effective incentive allocation via structured counterfactual inference

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:34.516301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f8d0f7fe-ba19-41d6-8c2f-5cdf0b1a69bf · outbound

This paper cites Invariant causal representation learning for out-of-distribution generalization.

Learning Treatment Representations for Downstream Instrumental Variable Regression Invariant causal representation learning for out-of-distribution generalization

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:34.282572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation aa6375a1-de70-4557-ad41-5ddbb39c471e · outbound

This paper cites Causal discovery with general non-linear relationships using non-linear ica.

Learning Treatment Representations for Downstream Instrumental Variable Regression Causal discovery with general non-linear relationships using non-linear ica

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:34.100416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a52f696f-26ff-4bd3-bca1-88eeaf502778 · outbound

This paper cites Kernel conditional moment test via maximum moment restriction.

Learning Treatment Representations for Downstream Instrumental Variable Regression Kernel conditional moment test via maximum moment restriction

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:33.914572Z

Source-reported events for the cited work

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Observation 6cc4ec76-1689-40f1-883c-b2ac16ee0672 · outbound

This paper cites Semiparametric causal sufficient dimension reduction of multidimensional treatments.

Learning Treatment Representations for Downstream Instrumental Variable Regression Semiparametric causal sufficient dimension reduction of multidimensional treatments

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:33.789318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9c38c4b5-5cd3-4463-b9a0-234f3420dcad · outbound

This paper cites Kernel-based tests for joint independence.

Learning Treatment Representations for Downstream Instrumental Variable Regression Kernel-based tests for joint independence

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:33.611683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ae3cd32d-80cf-44c8-9fc2-c1f2e26f875f · outbound

This paper cites Waiting online versus in-person: An empirical study on outpatient clinic visit incompletion.

Learning Treatment Representations for Downstream Instrumental Variable Regression Waiting online versus in-person: An empirical study on outpatient clinic visit incompletion

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:33.458628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bb17a5af-e803-4d1e-a818-8594d8313e38 · outbound

This paper cites The use and interpretation of principal component analysis in applied research.

Learning Treatment Representations for Downstream Instrumental Variable Regression The use and interpretation of principal component analysis in applied research

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:33.305398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ac8e1a82-45f1-4c2b-a313-cf6615d9a679 · outbound

This paper cites Principal component analysis with instrumental variables as a tool for modelling composition data.

Learning Treatment Representations for Downstream Instrumental Variable Regression Principal component analysis with instrumental variables as a tool for modelling composition data

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:33.209759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c411b0ae-e9d7-4fc8-a8fd-a8083599d183 · outbound

This paper cites Identifying Representations for Intervention Extrapolation.

Learning Treatment Representations for Downstream Instrumental Variable Regression Identifying Representations for Intervention Extrapolation

Reference 30

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Source-reported events for the cited work

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Observation 317834c4-6e10-437f-88db-af52b9970a8c · outbound

This paper cites Toward causal representation learning.

Learning Treatment Representations for Downstream Instrumental Variable Regression Toward causal representation learning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:33.054650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 04777d0a-5045-4dae-b118-960265fed478 · outbound

This paper cites Estimating Wage Disparities Using Foundation Models.

Learning Treatment Representations for Downstream Instrumental Variable Regression Estimating Wage Disparities Using Foundation Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T11:37:32.736332Z

Source-reported events for the cited work

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Observation 36014e5b-df4d-4dc4-a5b9-994fd1c537e7 · outbound

This paper cites Counterfactual and Synthetic Control Method: Causal Inference with Instrumented Principal Component Analysis.

Learning Treatment Representations for Downstream Instrumental Variable Regression Counterfactual and Synthetic Control Method: Causal Inference with Instrumented Principal Component Analysis

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:37:32.803161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a3ef9eb4-dde9-48cf-addd-ca69129ecfeb · outbound

This paper cites Constrained principal component analysis: A comprehensive theory.

Learning Treatment Representations for Downstream Instrumental Variable Regression Constrained principal component analysis: A comprehensive theory

Reference 35

Resolution: verified exact; doi, observed 2026-08-07T11:37:32.780030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ebd50fcf-2f37-4d8a-af60-c187d8928cb8 · outbound

This paper cites an unresolved cited work.

Learning Treatment Representations for Downstream Instrumental Variable Regression Unresolved cited work

Reference 36

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:37:32.977641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.