Paper Citation Record · LEDGER

Applied Causal Inference Powered by ML and AI

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2403.02467.

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

pith.paper-citation-record.v1
2403.02467 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:11:53.731974Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

36
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation beda7c65-e6dc-41c7-8c8b-25c713606a08 · inbound

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift cites this paper.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Applied Causal Inference Powered by ML and AI

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T13:11:53.731974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8508c037-af27-41ce-8a36-87b6559757b6 · inbound

Evaluating Program Sequences with Double Machine Learning: An Application to Labor Market Policies cites this paper.

Evaluating Program Sequences with Double Machine Learning: An Application to Labor Market Policies Applied Causal Inference Powered by ML and AI

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T01:08:43.632207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b202ec03-6013-48f3-82ce-ab1b7cd9e2e2 · inbound

Strategic A/B testing via Maximum Probability-driven Two-armed Bandit cites this paper.

Strategic A/B testing via Maximum Probability-driven Two-armed Bandit Applied Causal Inference Powered by ML and AI

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T22:13:05.100502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54898870-20c7-45e2-99b9-ea37453a2a9b · inbound

Shrinkage-Based Regressions with Many Related Treatments cites this paper.

Shrinkage-Based Regressions with Many Related Treatments Applied Causal Inference Powered by ML and AI

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T21:05:38.077600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f20e373d-8109-4403-a6fe-551e15de660a · inbound

From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies cites this paper.

From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies Applied Causal Inference Powered by ML and AI

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T17:13:53.451469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06734259-93aa-4560-9331-850988fccbb4 · inbound

Bayesian implementation of Targeted Maximum Likelihood Estimation for uncertainty quantification in causal effect estimation cites this paper.

Bayesian implementation of Targeted Maximum Likelihood Estimation for uncertainty quantification in causal effect estimation Applied Causal Inference Powered by ML and AI

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T15:33:58.591873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6593ce65-d446-4097-a382-0e457d057831 · inbound

Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases cites this paper.

Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases Applied Causal Inference Powered by ML and AI

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T02:51:56.828265Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc80c2ca-058d-4a9b-96e7-0295839cc55e · inbound

Causal Multi-Task Demand Learning cites this paper.

Causal Multi-Task Demand Learning Applied Causal Inference Powered by ML and AI

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-16T02:27:09.779587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 52b10efb-f21f-49a8-84f1-8fab1d254f94 · inbound

Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning cites this paper.

Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning Applied Causal Inference Powered by ML and AI

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:56:36.406782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fd41b5f9-a533-43f6-8cfb-637647ee6fdf · inbound

Decision-Focused Learning: When and Why Traditional Prediction Models Fail cites this paper.

Decision-Focused Learning: When and Why Traditional Prediction Models Fail Applied Causal Inference Powered by ML and AI

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-04T06:29:38.081710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9b0bbbc8-9dbd-4007-90a3-e20746438b9b · inbound

From Subgroups to Population Composition: A Transportability Approach to Effect Heterogeneity cites this paper.

From Subgroups to Population Composition: A Transportability Approach to Effect Heterogeneity Applied Causal Inference Powered by ML and AI

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T08:27:48.192018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6fcf8aa0-550a-493b-839e-b9f6ec2bceb6 · inbound

A Machine-Learning-Compatible Omnibus Test for Treatment Effect Heterogeneity cites this paper.

A Machine-Learning-Compatible Omnibus Test for Treatment Effect Heterogeneity Applied Causal Inference Powered by ML and AI

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-07-08T06:54:44.650310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.