On the persistent mischaracterization of Google and Facebook A/B tests: How to conduct and report online platform studies
Bibliographic record
Abstract
Marketing research has increasingly relied on online platform studies , which are studies conducted in a naturalistic online environment and which leverage the A/B testing tool provided by platforms such as Facebook or Google Ads. These studies allow researchers to compare the effectiveness of different ads and the way they are delivered, and to study “real” consumer behavior, such as clicking on ads. However, they lack true random assignment of ads to consumers, preventing causal inference. In this manuscript, we present a comprehensive review of 133 published online platform studies revealing how researchers have, so far, utilized and characterized these studies; we find that most of these studies are mistakenly presented as (randomized) experiments and most of their findings are erroneously described as causal. Our review suggests limited awareness of the inherent confoundedness of online platform studies (i.e., the inability to attribute user responses to ad creatives versus the platform’s targeting algorithms). Importantly, the prevalence of these undesirable practices has remained relatively constant over time. Against this backdrop, we offer clear guidance on how to position, conduct, and report online platform studies for researchers interested in this method and for reviewers invited to evaluate it.
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How this classification was reachedexpand
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 | 0.012 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".