The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
From predictive uplift modeling to prescriptive uplift analytics: A practical approach to treatment optimization while accounting for estimation risk
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In this research work, the problem was formulated as a multiple treatment optimization model with binary decision variables.”
From Prescriptive analytics: Literature review and research challenges (2020) · cites this paper as Lo & Pachamanova, 2015
Citations
Cites
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1993) · doi:10.1007/978-1-4899-4541-9 · Cited by 21,230
Goldberg, D. E., & Shakespeare, W. (2002) · Cited by 16,088
An Introduction to Statistical Learning
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013) · Springer Texts in Statistics · doi:10.1007/978-1-4614-7138-7 · Cited by 12,106
Bootstrap Methods and their Application
Davison, A. C., & Hinkley, D. V. (1997) · doi:10.1017/cbo9780511802843 · Cited by 7,350
Bootstrap Methods and Their Application
Loughin, T. M., Davison, A. C., & Hinkley, D. V. (1999) · Journal of the American Statistical Association · doi:10.2307/2669707 · Cited by 5,418
An Introduction to Statistical Learning: with Applications in R
James, G. M., Witten, D., Hastie, T. J., & Tibshirani, R. J. (2018) · doi:10.25334/q4ht55 · Cited by 2,912
Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009) · Princeton University Press eBooks · doi:10.1515/9781400831050 · Cited by 2,895
Genetic Algorithms in Search, Optimization & Machine Learning
Goldberg, D. E. (1989) · Medical Entomology and Zoology · Cited by 2,304
Research works printed but not linked: 9
- Dasgupta, S., Papadimitriou, C.H. and Vazirani, U.V. (2006) Algorithms, New York, NY: McGraw-Hill.
- Haughton, D., Haughton, J. and Lo, V.S.Y. (2016) Cause-and-Effect Business Analytics, CRC/Chapman & Hall.
- Lund, B. (2012) Direct Marketing Profit Model. In: Proceedings of Midwest SAS Users Group, Paper CI-04.
- Markowitz, H. (1952) Portfolio selection. Journal of Finance VII (1): 77–91.
- Papadimitriou, C.H. and Steiglitz, K. (1998) Combinational Optimization: Algorithms and Complexity, Mineola, NY: Dover.
- Radcliffe, N.J. (2007a) Using control groups to target on predicted lift. DMA Analytic Annual Journal (Spring): 14–21.
- Radcliffe, N.J. (2007b) Generating Incremental Sales: Maximizing the Incremental Impact of Cross-Selling, Up-Selling and Deep-Selling Through Uplift Modelling. Stochastic Solutions Limited.
- Radcliffe, N.J. and Surry, P.D. (1999) Differential response analysis: Modeling true response by isolating the effect of a single action. Proceedings of Credit Scoring and Credit Control VI, Credit Research Centre, University of Edinburgh Management School.
- Taha, H.A. (2010) Operations Research. 9th edn. Prentice-Hall.
Cited by
Prescriptive analytics: Literature review and research challenges
Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.04.003 · Cited by 501
“Binary linear programming has been applied in cases when all of the variables are binary such when recommending products or services to customers (Lo & Pachamanova, 2015).”
Sheng, J., Amankwah‐Amoah, J., Khan, Z., & Wang, X. (2021) · British Journal of Management · doi:10.1111/1467-8551.12441 · Cited by 369
Devriendt, F., Moldovan, D., & Verbeke, W. (2018) · Big Data · doi:10.1089/big.2017.0104 · Cited by 116
Metadata
- Authors
- Lo, V. S. Y., & Pachamanova, D. A.
- Affiliations
- Babson College (US)
- Corresponding author
- Pachamanova, Dessislava A
- Venue
- Journal of Marketing Analytics
- Published
- 2015-06
- Volume
- 3
- Issue
- 2
- Pages
- 79-95
- ISSN
- 2050-3318 (ISSN-L)
- 2050-3326
- OpenAlex
- W1650532115
- Cited by
- 28
- References
- 43
- Citations by year
- 2024: 4 · 2023: 4 · 2022: 3 · 2021: 2 · 2020: 4 · 2019: 8 · 2018: 2 · 2017: 1
- FWCI
- 0.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of Marketing Analytics (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Keywords
- uplift modeling, treatment optimization, estimation risk, multiple treatments, prescriptive analytics, treatment responders, heuristic optimization
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: From predictive uplift modeling to prescriptive uplift analytics: A practical approach to treatment optimization while accounting for estimation risk. https://app.cochat.ai/papers/10.1057/jma.2015.5
- The paper
- Lo, V. S. Y., & Pachamanova, D. A. (2015). From predictive uplift modeling to prescriptive uplift analytics: A practical approach to treatment optimization while accounting for estimation risk. Journal of Marketing Analytics. https://doi.org/10.1057/jma.2015.5
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
