Improving uplift model evaluation on randomized controlled trial data
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Publication:6555155
DOI10.1016/j.ejor.2023.09.018MaRDI QIDQ6555155
Björn Bokelmann, Stefan Lessmann
Publication date: 14 June 2024
Published in: European Journal of Operational Research (Search for Journal in Brave)
Cites Work
- Generalized random forests
- Estimating causal effects with optimization-based methods: a review and empirical comparison
- Targeting customers under response-dependent costs
- Response transformation and profit decomposition for revenue uplift modeling
- Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
- High-dimensional regression adjustments in randomized experiments
- Semiparametric Efficiency in Multivariate Regression Models with Missing Data
- Policy Learning With Observational Data
- Quasi-oracle estimation of heterogeneous treatment effects
- Double/debiased machine learning for treatment and structural parameters
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