In: Journal of econometrics, 2013, vol. 175, no. 1, p. 1-21
We investigate the finite sample properties of a large number of estimators for the average treatment effect on the treated that are suitable when adjustment for observed covariates is required, like inverse probability weighting, kernel and other variants of matching, as well as different parametric models. The simulation design used is based on real data usually employed for the evaluation...
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In: Review of economics and statistics, 2017, vol. 99, no. 1, p. 180-183
Previous research found that less accommodating caseworkers are more success- ful in placing unemployed workers into employment. This paper explores the causal mechanisms behind this result using semiparametric mediation analysis. Analysing rich linked jobseeker-caseworker data for Switzerland, we find that the positive employment effects of less accommodating caseworkers are not driven by a ...
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In: Journal of health economics, 2015, vol. 43, p. 170-189
This paper investigates the average effects of (firm-provided) workplace health promotion measures in form of the analysis of sickness absenteeism and health circles/courses on labour market out- comes of the firms’ employees. Exploiting linked employer-employee panel data that consist of rich survey- based and administrative information on firms, workers and regions, we apply a flexible...
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In: Journal of Applied Econometrics, 2014, vol. 29, no. 6, p. 920-943
This paper demonstrates the identification of causal mechanisms of a binary treatment under selection on observables, (primarily) based on inverse probability weighting; i.e. we consider the average indirect effect of the treatment, which operates through an intermediate variable (or mediator) that is situated on the causal path between the treatment and the outcome, as well as the...
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In: Oxford bulletin of economics and statistics, 2015, vol. 77, no. 1, p. 129-151
In many empirical problems, the evaluation of treatment effects is complicated by sample selection so that the outcome is only observed for a non-random subpopulation. In the absence of instruments and/or tight parametric assumptions, treatment effects are not point identified, but can be bounded under mild restrictions. Previous work on partial identification has primarily focused on the...
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In: Journal of Applied Econometrics, 2015, vol. 30, no. 7, p. 1144-1168
Identification in most sample selection models depends on the independence of the regressors and the error terms conditional on the selection probability. All quantile and mean functions are parallel in these models; this implies that quantile estimators cannot reveal any—per assumption non-existing—heterogeneity. Quantile estimators are nevertheless useful for testing the conditional...
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(Working Papers SES ; 496)
This paper considers the evaluation of direct and indirect treatment effects, also known as mediation analysis, when outcomes are only observed for a subpopulation due to sample selection or outcome attrition. For identification, we combine sequential conditional independence assumptions on the assignment of the treatment and the mediator, i.e. the variable through which the indirect effect...
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In: European Journal of Clinical Pharmacology, 2012, vol. 68, no. 8, p. 1209-1219
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In: Medizinische Klinik, 2009, vol. 104, no. 3, p. 220-224
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(Working papers SES ; 495)
This paper proposes semi- and nonparametric methods for disentangling the total causal effect of a continuous treatment on an outcome variable into its natural direct effect and the indirect effect that operates through one or several intermediate variables or mediators. Our approach is based on weighting observations by the inverse of two versions of the generalized propensity score (GPS),...
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