An empirical study is performed exploring the sensitivity to hidden confounders of GANITE, a method for Individualized Treatment Effect (ITE) estimation. Most real world datasets do not measure all confounders and thus it is important to know how crucial this is in order to obtain comparable predictions. This is explored through the removal of confounders with varying strengths and by removing subsets of the confounders simultaneously. The sensitivity is measured through the change in Precision in Estimating Heterogeneous Effects (PEHE) and through the divergence in the estimation of Average Treatment Effect (ATE) from the GT. Experiments are performed on synthetic and semi-synthetic data. The number of removed hidden confounders increases the error and variability of predictions, both for ITE and ATE. The strength of the removed confounders does not show a conclusive relationship on the error metrics. The effect of removing confounders with different causal graphs is explored but fails to show any clear patterns due to the high variance of the results.