Individualized Multi-Treatment Response Curves Estimation using RBF-net with Shared Neurons

Heterogeneous treatment effect estimation is an important problem in precision medicine. Specific interests lie in identifying the differential effect of different treatments based on some external covariates. We propose a novel non-parametric treatment effect estimation method in a multi-treatment setting. Our non-parametric modeling of the response curves relies on radial basis function (RBF)-nets with shared hidden neurons. Our model thus facilitates modeling commonality among the treatment outcomes. The estimation and inference schemes are developed under a Bayesian framework and implemented …

differential medicine modeling neurons non-parametric novel parametric precision precision medicine stat.ap stat.me stat.ml treatment