Double-robust estimators for a target parameter make use of 2 statistical working models, but are consistent for that parameter as soon as one of both models is correct. In this research, we develop optimal techniques for fitting these working models, with the aim to drasically reduce the bias of the double-robust estimator when both working models are misspecified.