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Paper Citation Record · LEDGER

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text

As of 7 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2607.26309.

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

pith.paper-citation-record.v1
2607.26309 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T00:17:10.854012Z

measured 44 of 44 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

44 of 44 outbound references displayed

  • verified exact9
  • verified fuzzy0
  • unresolved35
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

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Outbound references

Observation 74edd934-8f13-4d5c-8abc-bd428b454dc7 · outbound

This paper cites Bayesian topic regres- sion for causal inference.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Bayesian topic regres- sion for causal inference

Reference 1

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Observation 32bd7989-6eb8-4ce8-90b7-87a43689a920 · outbound

This paper cites LEACE: Perfect linear concept erasure in closed form.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text LEACE: Perfect linear concept erasure in closed form

Reference 2

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source=pdf_text observed=2026-08-01T00:17:05.512768Z digest=sha256:ee9cffd901aa8627d84a9c2b461c6c77a7772d4b071a285beecf0c2f8689c7ac

Observation 41a2e937-29e1-4eb4-b64d-a625de6f5e52 · outbound

This paper cites Blei and John D.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Blei and John D

Reference 3

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source=pdf_text observed=2026-08-01T00:17:05.621668Z digest=sha256:44f1057b6ec79632a5f297f72afc0ee80cddce7870088a77d1d5597132140462

Observation 49c6b86e-80ef-4bee-811f-a45f1513e8fa · outbound

This paper cites Blei, Andrew Y.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Blei, Andrew Y

Reference 4

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source=pdf_text observed=2026-08-01T00:17:05.747105Z digest=sha256:84e84cf9694fa1739068237f4124ebd2590e5a3eb7f9001beddb4517b0b3496e

Observation a39a14d5-94b2-4352-a1a4-6368ecf15ee6 · outbound

This paper cites Choosing the number of topics in lda models — a monte carlo comparison of selection criteria.Journal of Machine Learning Research, 25(79):1–30, 2024.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Choosing the number of topics in lda models — a monte carlo comparison of selection criteria.Journal of Machine Learning Research, 25(79):1–30, 2024

Reference 5

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source=pdf_text observed=2026-08-01T00:17:05.839641Z digest=sha256:b2aedf3d92558d5ca8ee8a3e0cc7b9422c4c60b5bc8877f71d7fe1a2994a24e1

Observation 51c56923-dfbc-4a83-a803-bec974b8f846 · outbound

This paper cites Dealing with limited overlap in estimation of average treatment effects.Biometrika, 96(1):187–199, 2009.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Dealing with limited overlap in estimation of average treatment effects.Biometrika, 96(1):187–199, 2009

Reference 6

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Observation 124e84ce-6941-49e2-973e-a2557b45c916 · outbound

This paper cites Fong, Justin Grimmer, Margaret E.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Fong, Justin Grimmer, Margaret E

Reference 7

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source=pdf_text observed=2026-08-01T00:17:06.069341Z digest=sha256:8089229d54be01f392fd327ccf310f7451435b911e13d2ca7ff262075dfdefbf

Observation a9fdf2bd-8c2f-43af-84db-9dae4313585a · outbound

This paper cites Discovery of treatments from text corpora.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Discovery of treatments from text corpora

Reference 8

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation a499b3e3-4ba5-4bc3-b0b3-cb696261d23e · outbound

This paper cites Causal inference with latent treatments.American Journal of Political Science, 67(2):374–389, 2023.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Causal inference with latent treatments.American Journal of Political Science, 67(2):374–389, 2023

Reference 9

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source=pdf_text observed=2026-08-01T00:17:06.353849Z digest=sha256:5299a5e922721800711195ea04c00d74501f9cb4684cb973dba931ae41b3a9e4

Observation 3648b602-9879-42f0-a775-3b16bdb388f2 · outbound

This paper cites topicmodels: An r package for fitting topic models.Journal of Statistical Software, 40(13):1–30, 2011.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text topicmodels: An r package for fitting topic models.Journal of Statistical Software, 40(13):1–30, 2011

Reference 10

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Observation b3250aeb-cfb2-42ae-be2d-798245d224f6 · outbound

This paper cites Causal estimation for text data with (apparent) overlap violations,.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Causal estimation for text data with (apparent) overlap violations,

Reference 12

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source=pdf_text observed=2026-08-01T00:17:06.865474Z digest=sha256:79ef2e559c097dc616f81cb04c5e4d5e88ceffc72c0d76a779e78cc8bc59984c

Observation c33b88a6-1fab-47dc-8245-dd623a6decd7 · outbound

This paper cites John Harker.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text John Harker

Reference 13

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source=pdf_text observed=2026-08-01T00:17:07.061197Z digest=sha256:fceb1e2033f50dfdf25ca42f7a4cf5af31ba8f4df182cbba3910f51605553b3a

Observation 5273c699-e551-4b75-8473-b8156b46c562 · outbound

This paper cites Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders

Reference 14

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Observation 197bc682-51ce-42da-b30e-e728e24b9606 · outbound

This paper cites an essay on the logical foundations of survey sampling, part one.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text an essay on the logical foundations of survey sampling, part one

Reference 15

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Observation f2b9205c-9bb7-4704-be2b-36e0cce9f30f · outbound

This paper cites Johns Hopkins University Press, Baltimore, 1978.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Johns Hopkins University Press, Baltimore, 1978

Reference 16

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source=pdf_text observed=2026-08-01T00:17:07.183717Z digest=sha256:68464ff7780f3f12a999b40a132b4fe07010f703f6e082fa3669a7e339e40130

Observation 3fdb517c-46b7-4bc9-be95-19970320588d · outbound

This paper cites Using text-based causal inference to disentangle factors influencing online review ratings.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Using text-based causal inference to disentangle factors influencing online review ratings

Reference 17

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Observation 15f10490-1fff-41e6-bb6d-c268984719cb · outbound

This paper cites Sentiment analysis and subjectivity.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Sentiment analysis and subjectivity

Reference 18

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Observation 990664b7-01b9-4571-ad64-13f32a8acf34 · outbound

This paper cites Estimating the effect of exercising on users’ online behavior.Proceedings of the International AAAI Con- ference on Web and Social Media, 11(1):734–738, May 2017.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Estimating the effect of exercising on users’ online behavior.Proceedings of the International AAAI Con- ference on Web and Social Media, 11(1):734–738, May 2017

Reference 19

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation a010ff83-1e8e-404b-8a07-db3dc2755a98 · outbound

This paper cites Matching with text data: An experimental evaluation of methods for matching documents and of measuring match quality.Political Analysis, 28(4):445–468, 2020.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Matching with text data: An experimental evaluation of methods for matching documents and of measuring match quality.Political Analysis, 28(4):445–468, 2020

Reference 20

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source=pdf_text observed=2026-08-01T00:17:07.602656Z digest=sha256:c66faac70a97306d437fe8f011fda275695bd6b76e25c7ca0c63e3f523c8f105

Observation 0a9764cc-1635-45c2-8e55-cd67aa0dcf45 · outbound

This paper cites Leveraging text data for causal inference using electronic health records.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Leveraging text data for causal inference using electronic health records

Reference 21

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source=pdf_text observed=2026-08-01T00:17:07.772949Z digest=sha256:84ef7b5c027f6cb608c62457db4d1e3d4b3c49068e2d48cf7d62ac382c029bbc

Observation 66666f5f-3c61-44f3-b221-5c4511edc429 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 22

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Observation 692f83a1-fbda-4566-af22-12fd8ae0b808 · outbound

This paper cites Pedregosa, G.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Pedregosa, G

Reference 23

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Observation a89ee87e-9515-42cc-a411-e96d0fbde2ab · outbound

This paper cites Causal effects of linguistic properties.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Causal effects of linguistic properties

Reference 24

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Observation 486f027d-29a9-4383-8c6b-7235c48d48c5 · outbound

This paper cites Roberts, Brandon M.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Roberts, Brandon M

Reference 25

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Observation b7126c74-32b4-4059-9964-1747cabadc9d · outbound

This paper cites Robins, Andrea Rotnitzky, and Lue Ping Zhao.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Robins, Andrea Rotnitzky, and Lue Ping Zhao

Reference 26

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Observation 535342fd-4790-4f33-a418-3a7f1629682c · outbound

This paper cites Rosenbaum and Donald B.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Rosenbaum and Donald B

Reference 27

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Observation 6771ba19-a210-4408-9cac-732824d8f5f2 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 28

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Observation 97389a6c-a8d0-4539-810a-8db4faff7ee1 · outbound

This paper cites Estimating causal effects of tone in online debates.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Estimating causal effects of tone in online debates

Reference 29

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 2b5366d0-ec10-402c-90d5-0a6d7377c341 · outbound

This paper cites an unresolved cited work.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Unresolved cited work

Reference 30

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Observation 34ce0c1e-c233-4f51-930d-9d79cd4bf898 · outbound

This paper cites A design-based solution for causal inference with text: Can a language model be too large?,.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text A design-based solution for causal inference with text: Can a language model be too large?,

Reference 31

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Observation f300b8ae-3c30-4cbc-b4d3-205686bc4fce · outbound

This paper cites Adapting text embeddings for causal inference.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Adapting text embeddings for causal inference

Reference 32

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Observation 8f573399-d503-419e-9fa9-b44ac2b9359a · outbound

This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 33

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source=pdf_text observed=2026-08-01T00:17:09.523762Z digest=sha256:bd231c1930a8dbda5c5f3626e4e04b502bc98af6f42f182e01e40c9f3c8edd84

Observation 1a25f878-812e-47c9-a9be-3bb141e5fc66 · outbound

This paper cites Detecting politeness in natural language.The R Journal, 10(2):489–502, 2018.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Detecting politeness in natural language.The R Journal, 10(2):489–502, 2018

Reference 34

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Observation 85905a8f-d9db-4daa-807e-3066d87c03a9 · outbound

This paper cites an unresolved cited work.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Unresolved cited work

Reference 39

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Observation b63ff1a2-ebd8-4683-a771-d31b2ba32e3c · outbound

This paper cites We apply the same data split as in our masking approach, training on the train data and testing on the test data.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text We apply the same data split as in our masking approach, training on the train data and testing on the test data

Reference 40

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Observation 0cff86b6-f4ec-4ca6-b3d0-3f2b9b01a791 · outbound

This paper cites Again, we use the same training and test split our masking procedure employs.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Again, we use the same training and test split our masking procedure employs

Reference 41

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Observation 2c2d125d-cf6e-4c7e-b091-702a3713163e · outbound

This paper cites an unresolved cited work.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Unresolved cited work

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-01T00:17:10.394366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T00:17:10.394366Z digest=sha256:a3acc6e1d6aab5582edf889ac794125f278c4b25fecd2016d8fea4ded7a2afce

Observation 257a6c17-8d6a-47e0-91ab-6927583e2ef3 · outbound

This paper cites Many estimated propensity scores are degenerate (exactly 0 or 1); not doing so leads to unstable andNaNATE estimates.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Many estimated propensity scores are degenerate (exactly 0 or 1); not doing so leads to unstable andNaNATE estimates

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-01T00:17:10.507644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T00:17:10.507644Z digest=sha256:99a8dd36ae5af13b8678e31f6d0c70d2c32084ff87bae919a862bb49bcc02a97

Observation a77db00e-c8d5-4775-9982-1853d80380ca · outbound

This paper cites an unresolved cited work.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Unresolved cited work

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-01T00:17:10.676272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T00:17:10.676272Z digest=sha256:b76e5c38af958ed76a161e64365e0d8d810287b2ef9577de2e1f4a89e0c3b32e

Observation fedfbc1a-bd7f-4997-890b-6f9b8e988ca8 · outbound

This paper cites D.7 Evaluation on Masked Text Section 3.2 introduced the design choice to train on masked text but evaluate on the original document.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text D.7 Evaluation on Masked Text Section 3.2 introduced the design choice to train on masked text but evaluate on the original document

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-01T00:17:10.854012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T00:17:10.854012Z digest=sha256:479f2fa576e84baf6baa95536d1fb434a3d08bead28d8479c7bb02e1721ae63e

Observation 01cff87a-587b-4e43-adad-62c5fc3cca96 · outbound

This paper cites doi: 10.18653/v1/2021.naacl-main.323.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text doi: 10.18653/v1/2021.naacl-main.323

Reference 2021

Resolution
verified exact
doi, observed 2026-08-01T00:21:44.213018Z

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.

source=pdf_text observed=2026-08-01T00:17:08.196975Z digest=sha256:aed0a6690eeeb029edd01bbd8233f65c7c4201864df1519ac6d3cadde0135a28

Observation f04c06d9-a0c3-4bfa-ab9c-68b184d00a11 · outbound

This paper cites URL https://www.science.org/doi/abs/10.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text URL https://www.science.org/doi/abs/10

Reference 2022

Resolution
verified exact
doi, observed 2026-08-01T00:21:44.894668Z

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.

source=pdf_text observed=2026-08-01T00:17:06.165766Z digest=sha256:45b99ce7660cc340db777665dbef28548a7c8d460b1537889f01f01230f39324

Observation 37c5cbf9-667f-4eaf-aafe-d65c0f7c79ee · outbound

This paper cites Causal Estimation for Text Data with (Apparent) Overlap Violations.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Causal Estimation for Text Data with (Apparent) Overlap Violations

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-01T00:17:07.005367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T00:17:07.005367Z digest=sha256:7b821268aa9f4407377a6e333c4a6e9ead15a099cb465ae72d140673a28e7da0

Observation a2b076f3-a5d3-4f53-8c63-8210ebb30a0b · outbound

This paper cites an unresolved cited work.

The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text Unresolved cited work

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-01T00:17:09.261602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T00:17:09.261602Z digest=sha256:0b55cf1739d0f4ea7f77fc64ae6a6979eb0759ef866e64ffd9419cd44688be3e

Pith citing papers

No inbound Pith citation observations are available.