selection) in matching, instrumental variable and control functions methods. (2) It contrasts the roles of exclusion … restrictions in matching and selection models. (3) It characterizes the sensitivity of matching to the choice of conditioning …. (4) It demonstrates the problem of choosing the conditioning variables in matching and the failure of conventional model …

The probability of selection into treatment plays an important role in matching and selection models. However, this … note establishes that the selection and matching procedures can be implemented using propensity scores fit on choice …

Propensity score matching estimators (Rosenbaum and Rubin, 1983) are widely used in evaluation research to estimate … average treatment effects. In this article, we derive the large sample distribution of propensity score matching estimators …. Our derivations take into account that the propensity score is itself estimated in a first step, prior to matching. We …

matching estimators exhibit the opposite behavior: they limit interpolation bias at the potential expense of extrapolation bias …. We propose combining the matching and synthetic control estimators through model averaging to create an estimator called … the SC, matching, MASC and penalized SC estimators do (and do not) perform well. Then, we use the MASC re-examine the …

Matching estimators are widely used for the evaluation of programs or treatments. Often researchers use bootstrapping … neighbor matching, the standard conditions for the bootstrap are not satisfied, leading the bootstrap variance to diverge from …

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