Weighted methods based on Inverse Probability Weights (IPW) have been widely used to estimate the effect of a treatment on an outcome using observational data. Despite their wide use, IPW methods rely on the correct specification of the propensity score model, which violations lead to biased estimates, and on the positivity assumption which practical violations lead to extreme weights and erroneous inferences. In addition, IPW-based methods do not target covariate balance, which is crucial in obtaining unbiased estimates of treatment effects in observational studies. In this talk, I will present several convex-optimization based approaches to find weights that optimize covariate-balance. These methods mitigate possible model misspecification while simultaneously controlling for precision. Specifically, I will describe two methods that find weights that balance confounders to estimate the effect of binary and continuous treatments on continuous and time-to-event outcomes. I will also describe a method that finds weights that optimally balance time-dependent confounders for marginal structural models. I will present these approaches using HIV, spine surgery, and heart disease data.