Estimating Continuous Treatment Effects with Two-Stage Kernel Ridge Regression

ArXi:2604.13410v1 Announce Type: cross We study the problem of estimating the effect function for a continuous treatment, which maps each treatment value to a population-averaged outcome. A central challenge in this setting is confounding: treatment assignment often depends on covariates, creating selection bias that makes direct regression of the response on treatment unreliable. To address this issue, we propose a two-stage kernel ridge regression method.