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

The Bias-Variance Tradeoff of Doubly Robust Estimator with Targeted $L_1$ regularized Neural Networks Predictions