Paper Citation Record · LEDGER

Dynamic Synthetic Controls vs. Panel-Aware Double Machine Learning for Geo-Level Marketing Impact Estimation

As of 19 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2508.20335.

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

pith.paper-citation-record.v1
2508.20335 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:11:19.001231Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

16 of 16 outbound references displayed

verified exact5
verified fuzzy5
unresolved4
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a10f568a-a624-4855-bebe-e7e29a5d8a06 · outbound

This paper cites Synthetic control meth- ods for comparative case studies: Estimating the effect of california’s tobacco control program.

Dynamic Synthetic Controls vs. Panel-Aware Double Machine Learning for Geo-Level Marketing Impact Estimation Synthetic control meth- ods for comparative case studies: Estimating the effect of california’s tobacco control program

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:20.876123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 520e702f-7a95-4986-a8f5-4376b22bab29 · outbound

This paper cites Understanding guest preferences and optimizing marketplace outcomes: A causal inference approach.

Dynamic Synthetic Controls vs. Panel-Aware Double Machine Learning for Geo-Level Marketing Impact Estimation Understanding guest preferences and optimizing marketplace outcomes: A causal inference approach

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:20.751573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e34d627f-aca6-4bfb-9638-3a6dd97cf5f3 · outbound

This paper cites Hirshberg, Guido W.

Dynamic Synthetic Controls vs. Panel-Aware Double Machine Learning for Geo-Level Marketing Impact Estimation Hirshberg, Guido W

Reference 3

Resolution: verified exact; doi, observed 2026-08-05T15:11:19.592131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 81499af3-2a8a-46ec-88ed-b0b0a5fd0907 · outbound

This paper cites The augmented synthetic control method.

Dynamic Synthetic Controls vs. Panel-Aware Double Machine Learning for Geo-Level Marketing Impact Estimation The augmented synthetic control method

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T15:11:18.660434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ca559f0-d9bf-4fb6-b34b-202f3556a617 · outbound

This paper cites augsynth: The Augmented Syn- thetic Control Method, 2021.

Dynamic Synthetic Controls vs. Panel-Aware Double Machine Learning for Geo-Level Marketing Impact Estimation augsynth: The Augmented Syn- thetic Control Method, 2021

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:20.534206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 955fc710-3940-4322-874f-7bac595aec2c · outbound

This paper cites Practical Marketplace Optimization at Uber Using Causally-Informed Machine Learning.

Dynamic Synthetic Controls vs. Panel-Aware Double Machine Learning for Geo-Level Marketing Impact Estimation Practical Marketplace Optimization at Uber Using Causally-Informed Machine Learning

Reference 6

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T15:11:19.802410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a7eebebb-6a0b-4dda-8d70-a8c0bf6566e6 · outbound

This paper cites Double/debiased machine learning for treatment and structural parameters.

Dynamic Synthetic Controls vs. Panel-Aware Double Machine Learning for Geo-Level Marketing Impact Estimation Double/debiased machine learning for treatment and structural parameters

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:20.453397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2454a8fd-ef3d-4185-827d-156b07828793 · outbound

This paper cites Double Machine Learning for Static Panel Models with Fixed Effects.

Dynamic Synthetic Controls vs. Panel-Aware Double Machine Learning for Geo-Level Marketing Impact Estimation Double Machine Learning for Static Panel Models with Fixed Effects

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:11:19.374745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c7f3d1a8-693b-406a-a6a6-6b6863e898f5 · outbound

This paper cites Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions.

Dynamic Synthetic Controls vs. Panel-Aware Double Machine Learning for Geo-Level Marketing Impact Estimation Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T15:11:18.824748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 476b6d1a-e9c8-4add-9243-04df16bec69e · outbound

This paper cites Valid and Unobtrusive Measurement of Returns to Advertising through Asymmetric Budget Split.

Dynamic Synthetic Controls vs. Panel-Aware Double Machine Learning for Geo-Level Marketing Impact Estimation Valid and Unobtrusive Measurement of Returns to Advertising through Asymmetric Budget Split

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:11:19.715896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c91228e2-c515-44f2-b215-18f744c51205 · outbound

This paper cites an unresolved cited work.

Dynamic Synthetic Controls vs. Panel-Aware Double Machine Learning for Geo-Level Marketing Impact Estimation Unresolved cited work

Reference 12

Resolution: malformed identifier; no resolver link, observed 2026-08-05T15:11:18.911658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2499f067-1b73-455e-a362-8cab44b22ff6 · outbound

This paper cites Estimating dynamic treatment effects in event studies with heterogeneous treatment effects.

Dynamic Synthetic Controls vs. Panel-Aware Double Machine Learning for Geo-Level Marketing Impact Estimation Estimating dynamic treatment effects in event studies with heterogeneous treatment effects

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T15:11:18.964760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b86bea3c-9234-4ba3-9a9e-cd4697a809f6 · outbound

This paper cites Geolift: Measuring incremental impact of adver- tising.

Dynamic Synthetic Controls vs. Panel-Aware Double Machine Learning for Geo-Level Marketing Impact Estimation Geolift: Measuring incremental impact of adver- tising

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:11:20.362509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ea195be1-48c9-46be-917f-bbbd3526f8bc · outbound

This paper cites an unresolved cited work.

Dynamic Synthetic Controls vs. Panel-Aware Double Machine Learning for Geo-Level Marketing Impact Estimation Unresolved cited work

Reference 2010

Resolution: verified exact; raw_fallback, observed 2026-08-05T15:11:20.260799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6ebdc0c9-c15b-43df-a479-25d8cd692cc9 · outbound

This paper cites an unresolved cited work.

Dynamic Synthetic Controls vs. Panel-Aware Double Machine Learning for Geo-Level Marketing Impact Estimation Unresolved cited work

Reference 2018

Resolution: verified exact; doi, observed 2026-08-05T15:11:19.494750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 08449ef3-f058-4c25-af4d-1731c06e31c6 · outbound

This paper cites an unresolved cited work.

Dynamic Synthetic Controls vs. Panel-Aware Double Machine Learning for Geo-Level Marketing Impact Estimation Unresolved cited work

Reference 2024

Resolution: unresolved; raw_fallback, observed 2026-08-05T15:11:20.617541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.