This paper enhances uplift modeling for multi-treatment marketing campaigns.
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5 results for “multi-treatment”
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.
Unified framework for response-adaptive targeting in multi-treatment experiments
problem Improving ethical and statistical efficiency in multi-treatment clinical trials
method Response-adaptive targeting strategies
result Unified framework for -Rebalancing Targeting Strategies (RTS)
M3E2 neural network estimates multiple treatment effects.
problem Estimating effects of multiple treatments simultaneously.
method Multi-task learning neural network model for multiple treatments, continuous and binary.
result M3E2 outperforms baselines in synthetic datasets.
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 …