Review of Deep Learning Methods for Individual Treatment Effect
Estimation with Automatic Hyperparameter Optimization
Abstract
Abstract—Estimation of individual treatment effect (ITE) for different
types of treatment is a common challenge in therapy assessments,
clinical trials and diagnosis. Deep learning methods, namely
representation based, adversarial, and variational, have shown promising
potential in ITE estimation. However, it was unclear whether the
hyperparameters of the originally proposed methods were well optimized
for different benchmark datasets. To solve these problems, we created a
public code library containing representation-based, adversarial, and
variational methods written in TensorFlow. In order to have a broader
collection of ITE estimation methods, we have also included neural
network based meta-learners. The code library is made accessible for
reproducibility and facilitating future works in the field of causal
inference. Our results demonstrate that performance of most methods can
be improved using automatic hyperparameter optimization. Additionally,
we review the methods and compare the performance of the optimized
models from our library on publicly available datasets. The potential of
hyperparameter optimization may encourage researchers to focus on this
aspect when creating new methods for inferring individual treatment
effect.