Author

Ying Jin

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Preprint Oct 2026

When Does Inexact Matching Ensure Balance and Inference without Adjustment?

One-to-one matching without replacement is a classical approach to constructing comparable treated and control samples in the design of observational studies. It pairs each treated unit with a distinct control while minimizing a covariate distance objective. With continuous covariates, the matched pairs generally remai...

Ying Jin · 0 citations

#machine learning Preprint Sep 2026

Conformal Policy Learning with Distribution-Free Safety Guarantees

Conformal policy learning (CPL) is proposed, a policy learning procedure with a new distribution-free safety guarantee that controls the probability of assigning treatment to an individual who would be harmed relative to control and achieves asymptotically optimal welfare subject to the safety constraint.

Ying Jin, Naoki Egami · 0 citations

Preprint Aug 2026

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...

Ying Jin, Dominik Rothenhäusler · 0 citations