Semisupervised score based matching algorithm to evaluate the effect of public health interventions

arXiv:2403.12367v1 Announce Type: cross
Abstract: Multivariate matching algorithms "pair" similar study units in an observational study to remove potential bias and confounding effects caused by the absence of randomizations. In one-to-one multivariate matching algorithms, a large number of "pairs" to be matched could mean both the information from a large sample and a large number of tasks, and therefore, to best match the pairs, such a matching algorithm with efficiency and comparatively limited auxiliary matching knowledge provided through a "training" …

abstract algorithm algorithms arxiv bias confounding cs.lg effects health information mean multivariate public public health stat.me stat.ml study the information type units