Abstract:
Uplift modeling is used extensively to identify treatment candidates that are more likely to benefit from an intervention. However, the fairness evaluation of such models...Show MoreMetadata
Abstract:
Uplift modeling is used extensively to identify treatment candidates that are more likely to benefit from an intervention. However, the fairness evaluation of such models remains a challenge due to the lack of ground truth on the outcome measure since a candidate cannot be in both treatment and control simultaneously. In this paper, we propose a framework that generates surrogate ground truth to serve as a proxy for counterfactual labels of uplm modeling campaigns. We then leverage the surrogate ground truth to conduct a more comprehensive binary fairness evaluation. We show how to apply the approach in a real-world marketing campaign for promotional offers and demonstrate its enhancement for fairness evaluation.
Date of Conference: 13-16 December 2021
Date Added to IEEE Xplore: 25 January 2022
ISBN Information:
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- IEEE Keywords
- Index Terms
- Fair Evaluation ,
- Ground Truth Generation ,
- Uplift Modeling ,
- Surrogate Ground Truth ,
- Marketing ,
- Comprehensive Evaluation ,
- Counterfactual ,
- Treatment Groups ,
- Advertising ,
- Adverse Impact ,
- Ideal Value ,
- Positive Activity ,
- Protective Properties ,
- XGBoost ,
- Binary Decision ,
- Department Of Labor ,
- AI Models ,
- Design Of Campaigns ,
- Starbucks
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Fair Evaluation ,
- Ground Truth Generation ,
- Uplift Modeling ,
- Surrogate Ground Truth ,
- Marketing ,
- Comprehensive Evaluation ,
- Counterfactual ,
- Treatment Groups ,
- Advertising ,
- Adverse Impact ,
- Ideal Value ,
- Positive Activity ,
- Protective Properties ,
- XGBoost ,
- Binary Decision ,
- Department Of Labor ,
- AI Models ,
- Design Of Campaigns ,
- Starbucks
- Author Keywords