Building confidence sets that reach their nominal level is especially hard in complex models and when the observed sample size is small, as in many likelihood‑free inference tasks. We introduce a simulation‑based calibration scheme that constructs local confidence sets using only samples drawn from the data‑generating model. Adapting insights from conformal prediction to parameter inference, the method forms data‑adaptive partitions that deliver finite‑sample local coverage and achieve conditional coverage as the number of simulations grows. It handles nuisance parameters with no substantial computational cost and computes uncertainty estimates that flag when more simulations are needed. Experiments on models with and without tractable likelihoods show that the resulting sets are better calibrated than existing alternatives. The procedure provides a practical tool for reliable inference in modern statistical applications.

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