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Background: The increased availability of claims data allows one to build high dimensional datasets, rich in covariates, for accurately estimating treatment eff

We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment to the treatment is unconfounded, that is, independe

Matching-type estimators using the propensity score are the major workhorse in active labour market policy evaluation. This work investigates if machine learnin

Many practical decision-making problems in economics and healthcare seek to estimate the average treatment effect (ATE) from observational data. The Double/Debi

We investigated the performance of four different propensity score (PS) methods to reduce selection bias in estimates of the average treatment effect (ATE) in o