Collaborative Heterogeneous Causal Inference Beyond Meta-analysis

arXiv:2404.15746v1 Announce Type: cross
Abstract: Collaboration between different data centers is often challenged by heterogeneity across sites. To account for the heterogeneity, the state-of-the-art method is to re-weight the covariate distributions in each site to match the distribution of the target population. Nevertheless, this method could easily fail when a certain site couldn't cover the entire population. Moreover, it still relies on the concept of traditional meta-analysis after adjusting for the distribution shift.
In this work, we propose a collaborative …

abstract analysis art arxiv beyond causal causal inference collaboration collaborative cs.cr cs.lg data data centers distribution inference match meta meta-analysis population state stat.ml type