This is a set of Python code that implements the data-driven procedure proposed in the paper https://arxiv.org/abs/1810.02905 that uses bootstrap aggregating to generate tight and statistically accurate confidence bounds for optimal values of stochastic optimization problems, or optimality gaps of arbitrarily given solutions.
See demo.ipynb for a demo on a use case of the code.