Augmented Inverse Hybrid Weighting: Robust Inference under Deterministic and Random Distribution Shifts
Reweighting source samples to match a target covariate distribution is a standard response to distribution shift when generalizing evidence from one population to another. This strategy is well suited to deterministic, learnable covariate discrepancies, but can be insufficient when source--target population differences also contain changes beyond covariate shift or when estimation of the density-ratio weights is unstable. To address this challenge, we introduce a new model that allows non-systematic changes between two population laws after systematic shifts are accounted for. Such residual shift is modeled as random perturbations to the probability space that cannot be represented in a l...
| Authors | Ying Jin, Ying Jin, Dominik Rothenhäusler |
|---|---|
| Primary category | stat.ME |
| Published | 1 August 2026 |
| Last updated at source | 1 August 2026 |
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