A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation
通过重要性加权扩散蒸馏进行因果估计的生成框架
Estimating individualized treatment effects from observational data is a central challenge in causal inference, largely due to covariate imbalance and confounding bias from non-randomized treatment assignment. While inverse probability weighting (IPW) is a well-established solution to this problem, its integration into modern deep learning frameworks remains limited. In this work, we propose Importance-Weighted Diffusion Distillation (IWDD), a novel generative framework that…