A simple and successful shrinkage method for weighting estimators of treatment effects

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractA simple shrinkage method is proposed to improve the performance of weighting estimators of the average treatment effect. As the weights in these estimators can become arbitrarily large for the propensity scores close to the boundaries, three different variants of a shrinkage method for the propensity scores are analyzed. The results of a comprehensive Monte Carlo study demonstrate that this simple method substantially reduces the mean squared error of the estimators in finite samples, and is superior to several popular trimming approaches over a wide range of settings.

Efficient Covariate Balancing for the Local Average Treatment Effect

“…and equivalently for the complementary weights. Under selection on observables, there is clear evidence that weight-normalized versions of inverse probability weighting estimators generally outperform their unweighted counterparts due to a reduction in variance (Busso et al, 2014;Pohlmeier et al, 2016). We expect these results to translate to the case of selection on unobservables.…”

Valid inference for treatment effect parameters under irregular identification and many extreme propensity scores

“…It is commonly preferred over the non-normalized inverse probability weighting (IPWI) due to its superior finite sample properties (Busso et al, 2014, Pohlmeier et al, 2016. The theory for IPWI can be constructed along the same lines.…”

“…Alternative approaches that do not trim but reweigh the sample are usually based on the propensity score as well, see e.g. Frölich (2004), Pohlmeier et al (2016) and Li et al (2018).…”

The effects of youth labour market reforms: evidence from Italian apprenticeships

“…Second, we estimate the ATT using the max trimming rule, and, third, on the untrimmed sample. Fourth, we use the shrinkage method of Pohlmeier et al (2014) on the IPW estimator by the cross-validation method.…”