• Corpus ID: 59553567

Causal Simulations for Uplift Modeling

@article{Berrevoets2019CausalSF,
  title={Causal Simulations for Uplift Modeling},
  author={Jeroen Berrevoets and Wouter Verbeke},
  journal={ArXiv},
  year={2019},
  volume={abs/1902.00287}
}
Uplift modeling requires experimental data, preferably collected in random fashion. This places a logistical and financial burden upon any organisation aspiring such models. Once deployed, uplift models are subject to effects from concept drift. Hence, methods are being developed that are able to learn from newly gained experience, as well as handle drifting environments. As these new methods attempt to eliminate the need for experimental data, another approach to test such methods must be… 
Optimising Individual-Treatment-Effect Using Bandits
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The uplifted contextual multi-armed bandit (U-CMAB), a novel approach to optimise the ITE by drawing upon bandit literature, is proposed, and experiments indicate that the proposed approach compares favourably against the state-of-the-art.

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