There is a growing interest in the development and application of optimization-based approaches to causal inference, especially for those that optimally balance covariates. This session will present novel optimization methods aimed at 1) estimating treatment effects from observational data, 2) learning optimal policies for personalized decision making, and 3) multiple testing.