Unmeasured confounding can cause considerable problems in observational studies and may threaten the validity of the estimates of causal treatment effects. There has been discussion on the amount of bias in treatment effect estimates that can occur due to unmeasured confounding. We investigate the robustness of a relatively new causal inference technique, targeted maximum likelihood estimation (TMLE), in terms of its robustness to the impact of unmeasured confounders. We benchmark TMLE’s performance with the inverse probability of treatment weighting (IPW) method. We utilize a plasmode-like simulation based on variables and parameters from the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments (SUPPORT). We evaluated the accuracy and precision of the estimated treatment effects. Though TMLE performed better in most of the scenarios considered, our simulation study results suggest that both methods performed reasonably well in estimating the marginal odds ratio, in the presence of unmeasured confounding. Nonetheless, the only remedy to unobserved confounding is controlling for as many as available covariates in an observational study, because not even TMLE can provide safeguard against bias from unmeasured confounders.