Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T00:10:25.763176Z
Paper Citation Record · LEDGER
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data
As of 17 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2412.19711.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
23 of 23 outbound references displayed
- verified exact3
- verified fuzzy13
- unresolved7
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation ebaec4df-d898-4c83-aabf-067e1b397b7b · outbound
This paper cites Causal inference using potential outcomes: Design, modeling, decisions.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data Causal inference using potential outcomes: Design, modeling, decisions
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:10:26.699549Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 819e5195-e558-4c3d-b631-d18b38966984 · outbound
This paper cites Metalearners for estimating heterogeneous treatment effects using machine learning.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data Metalearners for estimating heterogeneous treatment effects using machine learning
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:10:26.675942Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 6ed07a03-b11c-40fc-b5dd-f874c08a03a1 · outbound
This paper cites Towards optimal doubly robust estimation of heterogeneous causal effects.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data Towards optimal doubly robust estimation of heterogeneous causal effects
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:10:26.646811Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation b95a0b9b-4468-4774-b98b-292ee4fbbefc · outbound
This paper cites Quasi-oracle estimation of heterogeneous treatment effects.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data Quasi-oracle estimation of heterogeneous treatment effects
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:10:26.613146Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 75780979-19b8-46f8-9225-0389b57e963f · outbound
This paper cites Combining t-learning and dr-learning: a framework for oracle-efficient estimation of causal contrasts.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data Combining t-learning and dr-learning: a framework for oracle-efficient estimation of causal contrasts
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T00:10:25.590421Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c8c58bc3-7f63-4ae3-a611-9e5c6fa68b16 · outbound
This paper cites Randomized trials with missing outcome data: how to analyze and what to report.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data Randomized trials with missing outcome data: how to analyze and what to report
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:10:26.587266Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 3583f42f-efaf-40df-94c7-415ddec31aaa · outbound
This paper cites To impute or not to impute? missing data in treatment effect estimation.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data To impute or not to impute? missing data in treatment effect estimation
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:10:26.550658Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation bbb8646a-af4f-4c6b-a4e1-63f317c7ae2c · outbound
This paper cites Estimation of regression coefficients when some regressors are not always observed.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data Estimation of regression coefficients when some regressors are not always observed
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:10:26.521109Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation bc69fb25-d535-41ac-971a-c3ed92cfedf2 · outbound
This paper cites Stacked inverse probability of censoring weighted bagging: A case study in the infcarehiv register.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data Stacked inverse probability of censoring weighted bagging: A case study in the infcarehiv register
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:10:26.487817Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 2588779c-c753-4d2f-ad3f-3cbe64979f46 · outbound
This paper cites Causal inference in statistics: A primer.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data Causal inference in statistics: A primer
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:10:26.458297Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 355174d2-af72-40a6-a3b5-f065547a1cca · outbound
This paper cites On Weighted Orthogonal Learners for Heterogeneous Treatment Effects.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data On Weighted Orthogonal Learners for Heterogeneous Treatment Effects
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T00:10:25.654655Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a50c40a5-f68d-4aef-9ea5-f45f347b25e6 · outbound
This paper cites Demystifying statistical learning based on efficient influence functions.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data Demystifying statistical learning based on efficient influence functions
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:10:26.423989Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 16817170-297e-4986-9eb5-026ac897f8d4 · outbound
This paper cites Orthogonal statistical learning.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data Orthogonal statistical learning
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:10:26.399077Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 2a828b8a-bde4-415b-9dd2-2a0d9903b702 · outbound
This paper cites Sequential Double Robustness in Right-Censored Longitudinal Models.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data Sequential Double Robustness in Right-Censored Longitudinal Models
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T00:10:25.689434Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7ab046cc-ceb2-4da4-961f-8a34d42d526e · outbound
This paper cites Orthogonal prediction of counterfactual outcomes.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data Orthogonal prediction of counterfactual outcomes
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T00:10:25.696390Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7cb28f4d-2abc-424b-8fd7-6a3f3013c453 · outbound
This paper cites Super learner.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data Super learner
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:10:26.372463Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation f4087438-f820-489a-9d64-116c5e452660 · outbound
This paper cites Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T00:10:25.716721Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b090ead8-a6cf-469d-b0ff-e51db11d4c96 · outbound
This paper cites Regression in tensor product spaces by the method of sieves.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data Regression in tensor product spaces by the method of sieves
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:10:26.340319Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 4bd4b54b-e2b5-4a22-817e-4bd6fcdd5970 · outbound
This paper cites Debiased inference for a covariate-adjusted regression function.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data Debiased inference for a covariate-adjusted regression function
Reference 19
- Resolution
- verified exact
- local_arxiv, observed 2026-08-11T00:10:26.053458Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 070d5fc3-b40e-4714-93a8-9020e41d5bcc · outbound
This paper cites Flexibly Estimating and Interpreting Heterogeneous Treatment Effects of Laparoscopic Surgery for Cholecystitis Patients.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data Flexibly Estimating and Interpreting Heterogeneous Treatment Effects of Laparoscopic Surgery for Cholecystitis Patients
Reference 20
- Resolution
- verified exact
- local_arxiv, observed 2026-08-11T00:10:26.022428Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 9f0a027c-5291-4d1e-875d-000d432efa21 · outbound
This paper cites Order-Explicit Linearization of High-Dimensional $U$-Statistics.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data Order-Explicit Linearization of High-Dimensional $U$-Statistics
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T00:10:25.748005Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e4bc833a-f2ab-41f6-b402-b4487bf12c63 · outbound
This paper cites Estimation of subsidiary performance metrics under optimal policies.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data Estimation of subsidiary performance metrics under optimal policies
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T00:10:25.755612Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 91c6c4ed-51a8-4230-9de0-4101811d9e25 · outbound
This paper cites Recovering target causal effects from post-exposure selection induced by missing outcome data.
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data Recovering target causal effects from post-exposure selection induced by missing outcome data
Reference 23
- Resolution
- verified exact
- raw_fallback, observed 2026-08-11T00:10:25.932043Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Pith citing papers
No inbound Pith citation observations are available.