When studying causal relationships, balancing observed covariates is a critical part of the process to unbiasedly estimate a causal effect. For this, weighting techniques are often used by scientists of different fields, one of the most common techniques being inverse propensity score weighting. When applying propensity score methods, specifying the model to be used is one of the main challenges and the resulting weights usually does not target neither balance nor stability. In contrast, the recently proposed stable balancing weights directly target covariate imbalances and allow the researcher to explicitly set the desired balance constraints. In a time-to-event outcome setting, common in medical and epidemiological studies, the finite sample properties of the marginal hazard ratio estimator based on stable balancing weights are evaluated through Monte Carlo simulations. The results are compared to inverse propensity score weighting methods under diverse data generation settings.