Geometry Adaptive Counterfactual Distribution Learning with Diffusion-Guided Smoothing
We study counterfactual distribution learning for high-dimensional outcomes whose laws may concentrate near lower-dimensional structure. Standard isotropic smoothing ignores this geometry, leading to unfavorable scaling and unstable local inference. We propose semiparametrically debiased, diffusion-guided estimators for smoothed counterfactual densities and their scores. These estimators combine causal nuisance adjustment with geometry-adaptive localization driven by a learned diffusion score, yielding second-order nuisance remainders while aligning smoothing with local outcome geometry. We derive asymptotic expansions, integrated risk bounds, and simultaneous inference for smoothed densi...
| Authors | Kwangho Kim |
|---|---|
| Primary category | stat.ME |
| Published | 25 May 2026 |
| Last updated at source | 17 July 2026 |
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