  The Doubly Robust (DR) estimation of ATE can be carried out in 2 steps, where
in the first step, the treatment and outcome are modeled, and in the second
step the predictions are inserted into the DR estimator. The model
misspecification in the first step has led researchers to utilize Machine
Learning algorithms instead of parametric algorithms. However, existence of
strong confounders and/or Instrumental Variables (IVs) can lead the complex ML
algorithms to provide perfect predictions for the treatment model which can
violate the positivity assumption and elevate the variance of DR estimators.
Thus the ML algorithms must be controlled to avoid perfect predictions for the
treatment model while still learn the relationship between the confounders and
the treatment and outcome.
  We use two Neural network architectures and investigate how their
hyperparameters should be tuned in the presence of confounders and IVs to
achieve a low bias-variance tradeoff for ATE estimators such as DR estimator.
Through simulation results, we will provide recommendations as to how NNs can
be employed for ATE estimation.
