selection-on-observables type assumptions using matching or propensity score methods. Much of this literature is highly …

-nearest neighbour (KNN) matching estimators of average causal effects. This is an interesting result showing that bootstrap should not … valid inference for KNN matching estimators. We resample "estimated individual causal effects" (EICE), i.e. the difference … respect to the matching covariate, we obtain a bootstrap scheme valid also with heterogeneous causal effects where mild …

Propensity score matching estimators have two advantages. One is that they overcome the curse of dimensionality of … covariate matching, and the other is that they are nonparametric. However, the propensity score is usually unknown and needs to … in Smith and Todd (2005) that, if the unconfoundedness assumption fails, the matching results can be sensitive. However …