Inductive Domain Transfer In Misspecified Simulation-Based Inference
This work addresses scalability and generalization limitations in SBI for applications like medical biomarker estimation, though it appears incremental by building on RoPE with an inductive approach.
The paper tackles the problem of model misspecification in simulation-based inference (SBI) by proposing a fully inductive and amortized framework that integrates calibration and distributional alignment into a single, end-to-end trainable model, achieving performance that matches or surpasses existing methods like RoPE and other SBI estimators across synthetic and real-world benchmarks, including complex medical biomarker estimation.
Simulation-based inference (SBI) is a statistical inference approach for estimating latent parameters of a physical system when the likelihood is intractable but simulations are available. In practice, SBI is often hindered by model misspecification--the mismatch between simulated and real-world observations caused by inherent modeling simplifications. RoPE, a recent SBI approach, addresses this challenge through a two-stage domain transfer process that combines semi-supervised calibration with optimal transport (OT)-based distribution alignment. However, RoPE operates in a fully transductive setting, requiring access to a batch of test samples at inference time, which limits scalability and generalization. We propose here a fully inductive and amortized SBI framework that integrates calibration and distributional alignment into a single, end-to-end trainable model. Our method leverages mini-batch OT with a closed-form coupling to align real and simulated observations that correspond to the same latent parameters, using both paired calibration data and unpaired samples. A conditional normalizing flow is then trained to approximate the OT-induced posterior, enabling efficient inference without simulation access at test time. Across a range of synthetic and real-world benchmarks--including complex medical biomarker estimation--our approach matches or surpasses the performance of RoPE, as well as other standard SBI and non-SBI estimators, while offering improved scalability and applicability in challenging, misspecified environments.