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Paper Citation Record · LEDGER

Uplift modeling with continuous treatments: A predict-then-optimize approach

As of 23 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 3 inbound Pith citation observations for arXiv:2412.09232.

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

pith.paper-citation-record.v1
2412.09232 v2

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T17:18:23.294648Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:58:25.018609Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-09T06:05:36.462992Z

Reference resolution

15 of 15 outbound references displayed

  • verified exact0
  • verified fuzzy3
  • unresolved8
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4cad8f34-fac0-473c-a700-8757887db3f5 · outbound

This paper cites Fair Off-Policy Learning from Observational Data.

Uplift modeling with continuous treatments: A predict-then-optimize approach Fair Off-Policy Learning from Observational Data

Reference 5

Resolution
metadata mismatch
local_arxiv, observed 2026-08-11T17:18:23.730643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T17:18:23.168111Z digest=sha256:de31dc6828c87b3d7d8c3335b12936d663ba69209db085ed01a2ef01d34ef50d

Observation fef03f8d-e012-43cc-8195-c3ef9d07e9fa · outbound

This paper cites An Epistemic and Aleatoric Decomposition of Arbitrariness to Constrain the Set of Good Models.

Uplift modeling with continuous treatments: A predict-then-optimize approach An Epistemic and Aleatoric Decomposition of Arbitrariness to Constrain the Set of Good Models

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-11T17:18:23.204321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:18:23.204321Z digest=sha256:b8e89bcdcba7196b965a48139133633f7c7e910a70503746b7d0180296e38bc6

Observation 75e9ab20-f49d-49f1-a18e-f3698c7c9d6b · outbound

This paper cites Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities.

Uplift modeling with continuous treatments: A predict-then-optimize approach Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-11T17:18:23.217766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:18:23.217766Z digest=sha256:022fa9c4eddbb08902b1b0d5518d26c9b3c7e0062e3789e6345aa263448e6d6b

Observation 7b052434-7f01-4f30-914d-b275f1e153d3 · outbound

This paper cites VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments.

Uplift modeling with continuous treatments: A predict-then-optimize approach VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-11T17:18:23.223163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:18:23.223163Z digest=sha256:e021192f59139a34807f904cdd4b305c8746dabebe8200abe0e55fd176fdba98

Observation b85b6f29-4116-45c9-a342-5dcc2c153477 · outbound

This paper cites arXiv preprint arXiv:2407.03094.

Uplift modeling with continuous treatments: A predict-then-optimize approach arXiv preprint arXiv:2407.03094

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-11T17:18:23.229948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:18:23.229948Z digest=sha256:08a30ee13776b40f9204d19fb7b42bcfb1a1a59e398086d1f88095603a342637

Observation 5607f57c-608b-43e9-b7d6-83346b0d5f61 · outbound

This paper cites (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc.

Uplift modeling with continuous treatments: A predict-then-optimize approach (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:18:23.995143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T17:18:23.235916Z digest=sha256:45e6e8238e1f7400fe9316a7c95e8c57dab35de0be3b122f3a96e2124776fb74

Observation dbfda893-d5cf-49a9-8b57-20f01a83ad08 · outbound

This paper cites Metalearners for Ranking Treatment Effects.

Uplift modeling with continuous treatments: A predict-then-optimize approach Metalearners for Ranking Treatment Effects

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-11T17:18:23.242590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:18:23.242590Z digest=sha256:711d46203702a9aeb0730ccaf19317c4be875beabc8deaabe05ab6e0eca50000

Observation d59e39f0-bbb0-4d35-91b0-efcb7a0bf5b2 · outbound

This paper cites Thorax 78, 983–989.

Uplift modeling with continuous treatments: A predict-then-optimize approach Thorax 78, 983–989

Reference 14

Resolution
malformed identifier
raw_fallback, observed 2026-08-11T17:18:23.914039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T17:18:23.248381Z digest=sha256:69457a2d880b05032c246bab9c909b3662a77cad02dc6e24d4bac96a2fe4e42d

Observation 8fd314c1-9274-4066-9e25-d01f3f330ddf · outbound

This paper cites an unresolved cited work.

Uplift modeling with continuous treatments: A predict-then-optimize approach Unresolved cited work

Reference 2014

Resolution
unresolved
raw_fallback, observed 2026-08-11T17:18:24.236645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T17:18:23.003223Z digest=sha256:3bf8f19fb6f361b0b8c1878987a0312df5e0e58554d5e84c6a288ef97cb2e7a2

Observation 78792e3b-6fe2-4ada-beb7-f4ca0f3ebe70 · outbound

This paper cites Archives of toxicology 89, 2059–2068.

Uplift modeling with continuous treatments: A predict-then-optimize approach Archives of toxicology 89, 2059–2068

Reference 2015

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:18:24.119936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T17:18:23.197780Z digest=sha256:bc8ae9cfa126c9f7f45bd3aeea63d45d95d8050e4845bb1dd7696a326d55609e

Observation d156224f-a118-499d-9e9c-8a551574e4e8 · outbound

This paper cites Inherent Trade-Offs in the Fair Determination of Risk Scores.

Uplift modeling with continuous treatments: A predict-then-optimize approach Inherent Trade-Offs in the Fair Determination of Risk Scores

Reference 2016

Resolution
unresolved
no resolver link, observed 2026-08-11T17:18:23.209708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:18:23.209708Z digest=sha256:756dd28d6049357bc41770b49c27546ae3a83be10a70d16deac962421cf569a7

Observation eabe089f-8157-46d7-af56-cc9f105eeacb · outbound

This paper cites COM(2021) 206 final.

Uplift modeling with continuous treatments: A predict-then-optimize approach COM(2021) 206 final

Reference 2021

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:18:24.156085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T17:18:23.136054Z digest=sha256:78bad1f5ae12642fdf0b6eecaa08f1a8a9f9d7cf1dc74415197761fc4d3960e9

Observation 66aff737-c743-4c04-9a7f-add367718673 · outbound

This paper cites Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation.

Uplift modeling with continuous treatments: A predict-then-optimize approach Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-11T17:18:23.294648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:18:23.294648Z digest=sha256:004f26a309de6490077a0ffef5f2fe1b36ce62fc871e3af18f30fe9303aa5622

Observation fef1328c-e079-4e9b-8a90-d5f594543340 · outbound

This paper cites Using representation balancing to learn conditional-average dose responses from clustered data.

Uplift modeling with continuous treatments: A predict-then-optimize approach Using representation balancing to learn conditional-average dose responses from clustered data

Reference 2023

Resolution
metadata mismatch
local_arxiv, observed 2026-08-11T17:18:23.883649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T17:18:23.057368Z digest=sha256:5996238dd9ec0342f7b00a86885f19abab87b2aa697df1a584adbff24d872963

Observation 634e9880-2ddc-4860-b1d4-4fa889f60430 · outbound

This paper cites Sources of Gain: Decomposing Performance in Conditional Average Dose Response Estimation.

Uplift modeling with continuous treatments: A predict-then-optimize approach Sources of Gain: Decomposing Performance in Conditional Average Dose Response Estimation

Reference 2024

Resolution
metadata mismatch
local_arxiv, observed 2026-08-11T17:18:23.828107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T17:18:23.124181Z digest=sha256:a2ca28c1445b5f43fcef48e386fabd6b375324402339de8b1a935fd6912b8bc1

Pith citing papers

Observation ec43e4bd-0b3a-4622-884f-1a48aa62d12f · inbound

Hidden Representation Clustering with Multi-Task Representation Learning towards Robust Online Budget Allocation cites this paper.

Hidden Representation Clustering with Multi-Task Representation Learning towards Robust Online Budget Allocation Uplift modeling with continuous treatments: A predict-then-optimize approach

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-07T11:58:25.018609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:58:25.018609Z digest=sha256:f964fd32b2f6957be1434ad007a1f256b1847460bfaf6c88be33f9c50354b938

Observation 32e97ea3-d367-494f-a31f-28d80baedd08 · inbound

Principles and Guidelines for Randomized Controlled Trials in AI Evaluation cites this paper.

Principles and Guidelines for Randomized Controlled Trials in AI Evaluation Uplift modeling with continuous treatments: A predict-then-optimize approach

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-09T06:05:36.466027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-08T18:55:02.645026Z digest=sha256:13271cc32de3c13de77a3ed5613e2218e880c6afd87217e52f70904bf8f26218

Observation e8a9fe08-59e8-4735-b588-efcb03afdce3 · inbound

Principles and Guidelines for Randomized Controlled Trials in AI Evaluation cites this paper.

Principles and Guidelines for Randomized Controlled Trials in AI Evaluation Uplift modeling with continuous treatments: A predict-then-optimize approach

Reference 2026

Resolution
unresolved
no resolver link, observed 2026-08-02T15:01:23.802256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T15:01:23.802256Z digest=sha256:f29ea7b3e4fadc73a2519aae468bc6f16beea852d60f6be17dbdf51eafa5afe9