Covariate balance is crucial in obtaining unbiased estimates of treatment effects in observational studies. Methods based on propensity scores have been used to estimate treatment effects with observational data. These techniques however target accuracy instead of covariate balance. Methods that target covariate balance have been proposed and largely applied to estimate treatment effects on continuous outcomes. However, in many medical and epidemiological applications, the interest lies in estimating treatment effects on time-to-an-event outcomes. In this talk, we present robust orthogonality weights (ROW), a set of weights obtained by solving a quadratic constrained optimization problem that maximizes precision while constraining covariate balance defined as the sample correlation between confounders and treatment. By doing so, ROW optimally deal with both binary and continuous treatments. We evaluate the performance of the proposed weights in estimating hazard ratios of binary and continuous treatments with time-to-event outcomes in a simulation study. We apply ROW on two case studies using time-to-event data from the Women’s Health Initiative observational study.