The goal of matching is to reduce bias for the estimated treatment effect in an observational-data study, by finding, for every treated unit, one (or more) non-treated unit(s) with similar observable characteristics against which the covariates are balanced out (similar to the K-nearest neighbors algorithm).
[4] It was prominently criticized in economics by Robert LaLonde (1986),[7] who compared estimates of treatment effects from an experiment to comparable estimates produced with matching methods and showed that matching methods are biased.
Rajeev Dehejia and Sadek Wahba (1999) reevaluated LaLonde's critique and showed that matching is a good solution.
When the outcome of interest is binary, the most general tool for the analysis of matched data is conditional logistic regression as it handles strata of arbitrary size and continuous or binary treatments (predictors) and can control for covariates.
When the outcome of interest is continuous, estimation of the average treatment effect is performed.