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0111 · Jun 201919922001200920172026
5 results for multi-treatment

This paper enhances uplift modeling for multi-treatment marketing campaigns.

problem Optimizing marketing strategies by selecting individuals likely to respond to different treatments.
method Leveraging score ranking and calibration techniques.
result Improves overall performance of marketing campaigns.

Estimates individualized treatment effects using shared RBF-net neurons.

problem Identifying differential treatment effects based on covariates.
method Non-parametric radial basis function (RBF)-nets with shared hidden neurons in a Bayesian framework.
result Demonstrated through simulations and real data, the method identifies interesting treatment effects.

Exploratory cancer drug studies test multiple tumor cell lines against multiple candidate drugs. The goal in each paired (cell line, drug) experiment is to map out the dose-response curve of the cell line as the dose level of the drug increases. We propose Bayesian Tensor Filtering (BTF), a hierarchical Bayesian model …

2019-06-10abs ↗pdf ↗