Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation

Modern approaches for simulation-based inference rely upon deep learning
surrogates to enable approximate inference with computer simulators. In
practice, the estimated posteriors' computational faithfulness is, however,
rarely guaranteed. For example, Hermans et al. (2021) show that current
simulation-based inference algorithms can produce posteriors that are
overconfident, hence risking false inferences. In this work, we introduce
Balanced Neural Ratio Estimation (BNRE), a variation of the NRE algorithm
designed to produce posterior approximations that tend to be more conservative,
hence improving their reliability, while sharing the same Bayes optimal
solution. We achieve this by enforcing a balancing condition that increases the
quantified uncertainty in small simulation budget regimes while still
converging to the exact posterior as the budget increases. We provide
theoretical arguments showing that BNRE tends to produce posterior surrogates
that are more conservative than NRE's. We evaluate BNRE on a wide variety of
tasks and show that it produces conservative posterior surrogates on all tested
benchmarks and simulation budgets. Finally, we emphasize that BNRE is
straightforward to implement over NRE and does not introduce any computational
overhead.