MA-SBI: Misspecification-Aware Simulation-Based Inference via Side-Channel Guidance

• URL: http://arxiv.org/abs/2606.16923v1
• Date: Mon, 15 Jun 2026 16:26:42 GMT
• Title: MA-SBI: Misspecification-Aware Simulation-Based Inference via Side-Channel Guidance
• Abstract summary: Simulation-based inference of latent parameters is often hindered by simulator misspecification.<n> RoPE, the recent state-of-the-art for robust SBI, addresses this through optimal transport between learned representations of real and simulated observations.<n>We propose Misspecification-Aware Simulation-Based Inference (MA SBI), a calibration-free framework that turns this sidechannel into a posterior correction.
• Score: 1.8923689868452591
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Simulation-based inference (SBI) of latent parameters is often hindered by simulator misspecification, the mismatch between simulated and real-world observations caused by inherent modeling simplifications. RoPE, the recent state-of-the-art for robust SBI, addresses this through optimal transport between learned representations of real and simulated observations, but requires ground-truth parameter calibration pairs that are typically unavailable in the very settings where SBI is needed. What practitioners do have is unstructured side-information such as regime labels, instruction text, and policy bulletins. We propose Misspecification-Aware Simulation-Based Inference (MA-SBI), a calibration-free framework that turns this side-channel into a posterior correction. A learned corrector maps side-channel text to an observation-space shift applied before any pre-trained amortized posterior, requiring no retraining and no parameter ground-truth. Our main theorem bounds achievable bias reduction by the mutual information between misspecification and side-channel, with a non-vacuous constant that extends to all sub-Gaussian noise via Donsker-Varadhan. On hide-the-calibration benchmarks, MA-SBI with text alone matches the oracle posterior across 10 seeds and two backbones (TOST equivalence), while RoPE given more data does not. The two approaches are complementary: where misspecification is structural and recoverable from parameter pairs, RoPE dominates, as the theory predicts. A stochastic variant improves posterior-predictive log-likelihood on real COVID and OxCGRT epidemiological data, and correctly leaves the posterior unchanged on a well-specified cognitive-science corpus.

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