Statistical matching as an unconstrained optimisation problem
N. T. Longford
Abstract
Within the potential outcomes framework, balancing the treatment groups
is a key step in estimating the average treatment effect in an observational study.
Methods for optimal matching or weighting solve nonlinear programming problems.
We present an alternative, related to ridge regression.
Its solution has a closed form and is a smooth function of a set of tuning
parameters.
The method is accompanied by a simple way of exploring the sensitivity
with respect to bias due to an unobserved confounder.
It is applied to retrospective studies in neonatal research,
concerned with clinical care for preterm born babies
in the first few weeks of their lives.
Submitted.
January 2025.