  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
via an efficient Markov chain Monte Carlo algorithm, appropriately
accommodating uncertainty in all aspects of the analysis. The numerical
performance of the method is demonstrated through simulation experiments.
Applying our proposed method to MIMIC data, we obtain several interesting
findings related to the impact of different treatment strategies on the length
of ICU stay and 12-hour SOFA score for sepsis patients who are home-discharged.
