In this talk we present targeted maximum likelihood based estimators of a causal effect defined in realistic semiparametric models for the data generating experiment, that takes away the need for specifying regression models. Two fundamental concepts underlying this methodology are careful definition of the target parameter of the data generating distribution in a realistic semiparametric model, super Learning, i.e., the very aggressive use of cross-validation to select optimal combinations of many candidate estimators, and subsequent targeted maximum likelihood estimation to target the fit towards the causal effect/target parameter of interest. We demonstrate the performance in simulation studies. We also illustrate this method for assessing causal effects of treatment on clinical outcomes in RCT and observational studies in HIV.