Université de Fribourg

Bounds on direct and indirect effects under treatment/mediator endogeneity and outcome attrition

Huber, Martin ; Lafférs, Lukáš

(Working Papers SES ; 514)

Causal mediation analysis aims at disentangling a treatment effect into an indirect mechanism operating through an intermediate outcome or mediator, as well as the direct effect of the treatment on the outcome of interest. However, the evaluation of direct and indirect effects is frequently complicated by non-ignorable selection into the treatment and/or mediator, even after controlling for...

Université de Fribourg

Sharp bounds on causal effects under sample selection

Huber, Martin ; Mellace, Giovanni

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...

Université de Fribourg

A Test of the Conditional Independence Assumption in Sample Selection Models

Huber, Martin ; Melly, Blaise

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...

Université de Fribourg

The causalweight package for causal inference in R

Bodory, Hugo ; Huber, Martin

(Working Papers SES ; 493)

We describe R package “causalweight” for causal inference based on inverse probability weighting (IPW). The “causalweight” package offers a range of semiparametric methods for treatment or impact evaluation and mediation analysis, which incorporates intermediate outcomes for investigating causal mechanisms. Depending on the method, identification relies on selection on observables ...