Huber, Martin 2014 Sample selection and attrition are inherent in a range of treatment evaluation problems such as the estimation of the returns to schooling or training. Conventional estimators tackling selection bias typically rely on restrictive functional form assumptions that are unlikely to hold in reality. This paper shows identification of average and quantile treatment effects in the presence of the double selection problem into (i) a selective subpopulation (e.g., working—selection on unobservables) and (ii) a binary treatment (e.g., training—selection on observables) based on weighting observations by the inverse of a nested propensity score that characterizes either selection probability. Weighting estimators based on parametric propensity score models are applied to female labor market data to estimate the returns to education. application/pdf https://folia.unifr.ch/global/documents/307721 https://folia.unifr.ch/documents/307721/files/treatmentevaluationinthepresenceofsampleselection.pdf eng info:eu-repo/semantics/altIdentifier/doi/10.1080/07474938.2013.806197 info:eu-repo/semantics/openAccess License undefined Econometric reviews. - 2014, vol. 33, no. 8, p. 869-905 Inverse probability weighting Sample selection Treatment effects info:eu-repo/classification/udc/33 Treatment evaluation in the presence of sample selection http://purl.org/coar/resource_type/c_6501