Ghost Ads: Improving the Economics of Measuring Online Ad Effectiveness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To measure the effects of advertising, marketers must know how consumers would behave had they not seen the ads. The authors develop a methodology they call “ghost ads,” which facilitates this comparison by identifying the control group counterparts of the exposed consumers in a randomized experiment. The authors show that, relative to public service announcement and intent-to-treat A/B tests, ghost ads can reduce the cost of experimentation, improve measurement precision, deliver the relevant strategic baseline, and work with modern ad platforms that optimize ad delivery in real time. The authors also describe a variant, “predicted ghost ad” methodology, which is compatible with online display advertising platforms; their implementation records more than 100 million predicted ghost ads per day. The authors demonstrate the methodology with an online retailer's display retargeting campaign. They show novel evidence that retargeting can work: the ads lifted website visits by 17.2% and purchases by 10.5%. Compared with intent-to-treat and public service announcement experiments, advertisers can measure ad lift just as precisely while spending at least an order of magnitude less.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it