Paper Citation Record · LEDGER

Orthogonality-Constrained Deep Instrumental Variable Model for Causal Effect Estimation

As of 8 August 2026, this Paper Citation Record lists 9 of 9 outbound references and 0 inbound Pith citation observations for arXiv:2506.02790.

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

pith.paper-citation-record.v1
2506.02790 v1

Coverage vector

measured 9 of 9 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:19:21.196647Z

measured 9 of 9 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

9 of 9 outbound references displayed

verified exact1
verified fuzzy5
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2b367179-c253-42f3-ba28-7c88a8cb9270 · outbound

This paper cites The instrumental variable (IV) method, a classic approach to causal identification, plays a crucial role in addressing endogeneity issues.

Orthogonality-Constrained Deep Instrumental Variable Model for Causal Effect Estimation The instrumental variable (IV) method, a classic approach to causal identification, plays a crucial role in addressing endogeneity issues

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:23.333318Z

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 9b59ef18-bbcb-43b1-80c9-b269869a7b3a · outbound

This paper cites The instrumental variable (IV) method, a key tool for addressing endogeneity issues, still faces challenges in high-dimensional and complex data environments.

Orthogonality-Constrained Deep Instrumental Variable Model for Causal Effect Estimation The instrumental variable (IV) method, a key tool for addressing endogeneity issues, still faces challenges in high-dimensional and complex data environments

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:23.042725Z

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 80ce5b64-cf7f-45eb-aa86-60f3dcec2dab · outbound

This paper cites an unresolved cited work.

Orthogonality-Constrained Deep Instrumental Variable Model for Causal Effect Estimation Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:19:22.846895Z

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 6837fd53-1d68-43ef-b085-c2ec2f0b297a · outbound

This paper cites In the last 50 rounds, orthogonal constraint terms are gradually introduced to ensure stable optimisation and avoid excessive intervention in the learning process.

Orthogonality-Constrained Deep Instrumental Variable Model for Causal Effect Estimation In the last 50 rounds, orthogonal constraint terms are gradually introduced to ensure stable optimisation and avoid excessive intervention in the learning process

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:22.577865Z

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 4089715b-feb9-47fe-8a95-1f1f6c860139 · outbound

This paper cites Epoch {epoch+1}/{epochs}, Loss: {loss.item():.6f}, MSE: {mse_loss.item():.6f}.

Orthogonality-Constrained Deep Instrumental Variable Model for Causal Effect Estimation Epoch {epoch+1}/{epochs}, Loss: {loss.item():.6f}, MSE: {mse_loss.item():.6f}

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:22.355420Z

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 0bf07023-b3b4-40bd-9762-9c944fc92790 · outbound

This paper cites an unresolved cited work.

Orthogonality-Constrained Deep Instrumental Variable Model for Causal Effect Estimation Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:19:22.096867Z

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 0395864a-6b0d-4d13-9eed-b543e6b4a6f9 · outbound

This paper cites an unresolved cited work.

Orthogonality-Constrained Deep Instrumental Variable Model for Causal Effect Estimation Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:19:21.819637Z

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 4711cda9-5349-4a04-93cf-93077686837d · outbound

This paper cites These methods have opened new technical avenues for high-dimensional causal inference, addressing many modelling challenges in real-world data.

Orthogonality-Constrained Deep Instrumental Variable Model for Causal Effect Estimation These methods have opened new technical avenues for high-dimensional causal inference, addressing many modelling challenges in real-world data

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:21.619768Z

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 2ccc23f9-b556-41ab-8fb4-6488df503305 · outbound

This paper cites This section proposes potential future optimisation paths to further enhance the method's impact in both theoretical research and practical applications.

Orthogonality-Constrained Deep Instrumental Variable Model for Causal Effect Estimation This section proposes potential future optimisation paths to further enhance the method's impact in both theoretical research and practical applications

Reference 9

Resolution: verified exact; doi, observed 2026-08-07T11:19:21.377780Z

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.

Pith citing papers

No inbound Pith citation observations are available.