SUBCLASSIFICATION MATCHING METHOD FOR AVERAGE TREATMENT EFFECT AND A NUMERICAL COMPARISON OF RELATED METHODS

In recent years, attention has been focused on estimating average treatment effects in statistics, economics, epidemiology and so on. For example, effects of job training in economics, or comparing treatment effects in epidemiological studies are frequently studied. There is a lot of literature on estimating the average treatment effect of a binary treatment variable under some assumptions. Some of them use parametric methods, and some use semiparametric methods. This paper firstly describes the role of Rubin’s causal model, reviews various methods for estimating the average treatment effects, then proposes one combined method (subclassification matching method) to estimate the average treatment effect. Extensive simulations are carried to compare all the methods. We find that the proposed mixed methods are better than other methods considered here.