The DOI matches its Crossref record; 55 of the 57 references it cites were found in a registry or an index.
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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In the citing paper
“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
Citations
Cites
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879
Data Analysis Using Regression and Multilevel/Hierarchical Models
Gelman, A., & Hill, J. (2006) · doi:10.1017/cbo9780511790942 · Cited by 13,597
Rosenbaum, P. R., & Rubin, D. B. (1985) · The American Statistician · doi:10.2307/2683903 · Cited by 6,472
SOME PRACTICAL GUIDANCE FOR THE IMPLEMENTATION OF PROPENSITY SCORE MATCHING
Caliendo, M., & Kopeinig, S. (2008) · Journal of Economic Surveys · doi:10.1111/j.1467-6419.2007.00527.x · Cited by 6,238
Matching Methods for Causal Inference: A Review and a Look Forward
Stuart, E. A. (2010) · Statistical Science · doi:10.1214/09-sts313 · Cited by 5,478
Recent Developments in the Econometrics of Program Evaluation
Imbens, G. W., & Wooldridge, J. M. (2009) · Journal of Economic Literature · doi:10.1257/jel.47.1.5 · Cited by 4,926
Propensity Score-Matching Methods for Nonexperimental Causal Studies
Dehejia, R., & Wahba, S. (2002) · The Review of Economics and Statistics · doi:10.1162/003465302317331982 · Cited by 4,864
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Imbens, G. W., & Rubin, D. B. (2015) · doi:10.1017/cbo9781139025751 · Cited by 4,210
Identification and estimation of local average treatment effects
Imbens, G. W., & Angrist, J. D. (1994) · Econometrica · doi:10.2307/2951620 · Cited by 4,101
Research works printed but not linked: 10
- Abraham M, 2008, Harvard Bus. Rev., 86, 28
- Berkovich P, 2016, Nielsen J. Measurement
- Bickel P, 1997, Statistica Sinicia, 7, 1
- Eckles D, Bakshy E (2017) Bias and high-dimensional adjustment in observational studies of Peer effects. Working paper, MIT Sloan School of Management, Cambridge, MA.
- Ferraro PJ, 2017, J. Assoc. Environ. Resource Econom., 4, 281
- LaLonde RJ, 1986, Amer. Econom. Rev., 76, 604
- Oster E, 2017, J. Bus. Econom. Statist.
- Pham TT, Shen Y (2017) A deep causal inference approach to measuring the effects of forming group loans in online non-profit microfinance platform. Working paper, Stanford Graduate School of Business, Stanford, CA. Available at https://arxiv.org/pdf/1706.02795.pdf. · arXiv
- Rosenbaum P, 1983, J. Royal Statist. Soc. Ser. B, 45, 212
- Tibshirani R, 1996, J. Royal Statist. Soc., 58, 267
Cited by
Sorted by citations
The future of social media in marketing
Appel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00695-1 · Cited by 1,893
“More recently, the value of advertising on social media continues to be explored (e.g., Gordon et al. 2019), as well as how it interacts with other forms of media such as television (e.g., Fossen and Schweidel 2016, 2019) and affects new product adoption through diffusion of information mechanisms…”
BackgroundTrustworthy Online Controlled Experiments: A Practical Guide to A/B Testing
Kohavi, R., Tang, D., & Xu, Y. (2020) · doi:10.1017/9781108653985 · Cited by 297
Online and offline retailing: What we know and directions for future research
Ratchford, B. T., Soysal, G., Zentner, A., & Gauri, D. K. (2022) · Journal of Retailing · doi:10.1016/j.jretai.2022.02.007 · Cited by 232
Online Display Advertising Markets: A Literature Review and Future Directions
Choi, H., Mela, C. F., Balseiro, S. R., & Leary, A. (2020) · Information Systems Research · doi:10.1287/isre.2019.0902 · Cited by 181
Inefficiencies in Digital Advertising Markets
Gordon, B. R., Jerath, K., Katona, Z., Narayanan, S., Shin, J., & Wilbur, K. C. (2020) · Journal of Marketing · doi:10.1177/0022242920913236 · Cited by 195
Self-citation
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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