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Individualized Multi-Treatment Response Curves Estimation using RBF-net with Shared Neurons. (arXiv:2401.16571v1 [stat.ME])
stat.ML updates on arXiv.org arxiv.org
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 …
arxiv differential medicine modeling neurons non-parametric novel parametric precision precision medicine stat.me treatment