A New Transformation Approach for Uplift Modeling with Binary Outcome (2310.05549v1)
Abstract: Uplift modeling has been used effectively in fields such as marketing and customer retention, to target those customers who are more likely to respond due to the campaign or treatment. Essentially, it is a machine learning technique that predicts the gain from performing some action with respect to not taking it. A popular class of uplift models is the transformation approach that redefines the target variable with the original treatment indicator. These transformation approaches only need to train and predict the difference in outcomes directly. The main drawback of these approaches is that in general it does not use the information in the treatment indicator beyond the construction of the transformed outcome and usually is not efficient. In this paper, we design a novel transformed outcome for the case of the binary target variable and unlock the full value of the samples with zero outcome. From a practical perspective, our new approach is flexible and easy to use. Experimental results on synthetic and real-world datasets obviously show that our new approach outperforms the traditional one. At present, our new approach has already been applied to precision marketing in a China nation-wide financial holdings group.
- Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 113(27):7353–7360, 2016. doi: 10.1073/pnas.1510489113. URL https://www.pnas.org/doi/abs/10.1073/pnas.1510489113.
- Generalized random forests. The Annals of Statistics, 47(2):1148 – 1178, 2019. doi: 10.1214/18-AOS1709. URL https://doi.org/10.1214/18-AOS1709.
- A literature survey and experimental evaluation of the state-of-the-art in uplift modeling: A stepping stone toward the development of prescriptive analytics. Big Data, 6(1):13–41, 2018. doi: 10.1089/big.2017.0104. PMID: 29570415.
- Causal inference and uplift modelling: A review of the literature. In International Conference on Predictive Applications and APIs, 2016.
- Guyon, I. Design of experiments for the nips 2003 variable selection benchmark. In NIPS 2003 workshop on feature extraction and feature selection, 2003.
- Uplift modeling for clinical trial data. In ICML Workshop on Clinical Data Analysis, 2012.
- Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv: Statistics Theory, 2017.
- Causal effect inference with deep latent-variable models. In Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, pp. 6449–6459, Red Hook, NY, USA, 2017. Curran Associates Inc. ISBN 9781510860964.
- Radcliffe, N. Using control groups to target on predicted lift: Building and assessing uplift model. Direct Marketing Analytics Journal, pp. 14–21, 2007.
- The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1):41–55, 04 1983. ISSN 0006-3444. doi: 10.1093/biomet/70.1.41. URL https://doi.org/10.1093/biomet/70.1.41.
- Decision trees for uplift modeling. In 2010 IEEE International Conference on Data Mining, pp. 441–450, 2010. doi: 10.1109/ICDM.2010.62.
- Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113(523):1228–1242, 2018. doi: 10.1080/01621459.2017.1319839. URL https://doi.org/10.1080/01621459.2017.1319839.
- Ganite: Estimation of individualized treatment effects using generative adversarial nets. In ICLR, 2018.