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A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook
Abstract
Observational methods often fail to accurately recover the treatment effects generated from randomized advertising experiments on Facebook.
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“Large-scale studies suggest that most of the time, due to methodological issues, the efficiency of targeting is largely overstated (Gordon et al., 2019;Lewis and Reiley, 2014), and that, overall,”
From How online advertising targets consumers: The uses of categories and algorithmic tools by audience planners (2024) · cites this paper as Gordon et al., 2019
In the citing paper
“computer generated categories and optimization are not just overstated (Aral, 2020;Gordon et al., 2019), they also neither match the power of demographics as standards, nor the idiosyncratic meaningfulness of custom audiences.”
From How online advertising targets consumers: The uses of categories and algorithmic tools by audience planners (2024) · cites this paper as Gordon et al., 2019
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Metadata
- Authors
- Gordon, B. R., Zettelmeyer, F., Bhargava, N., & Chapsky, D.
- Affiliations
- Northwestern University (US)
- Meta (United States) (US)
- Corresponding author
- Gordon, Brett R.
- Zettelmeyer, Florian
- Chapsky, Dan
- Venue
- Marketing Science
- Published
- 2019-03
- Volume
- 38
- Issue
- 2
- Pages
- 193-225
- ISSN
- 0732-2399 (ISSN-L)
- 1526-548X
- OpenAlex
- W2564917231
- Cited by
- 287
- References
- 57
- Citations by year
- 2026: 41 · 2025: 38 · 2024: 24 · 2023: 41 · 2022: 35 · 2021: 31 · 2020: 45 · 2019: 14
- FWCI
- 62.78
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Marketing Science (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Keywords
- advertising measurement, randomized field experiment, observation method, treatment effect estimation, Facebook, social media advertising, causal inference
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