Université de Fribourg

Including Covariates in the Regression Discontinuity Design

Frölich, Markus ; Huber, Martin

In: Journal of Business and Economic Statistics, 2019, vol. 37, no. 4, p. 736-748

This article proposes a fully nonparametric kernel method to account for observed covariates in regression discontinuity designs (RDD), which may increase precision of treatment effect estimation. It is shown that conditioning on covariates reduces the asymptotic variance and allows estimating the treatment effect at the rate of one- dimensional nonparametric regression, irrespective of the...

Université de Fribourg

The Impact of Response Measures on COVID-19-Related Hospitalization and Death Rates in Germany and Switzerland

Huber, Martin ; Feuerriegel, Stefan

In: Swiss Journal of Economics and Statistics, 2020, vol. 156, no. 10, p. 1-19

We assess the impact of the timing of lockdown measures implemented in Germany and Switzerland on cumulative COVID-19-related hospitalization and death rates. Our analysis exploits the fact that the epidemic was more advanced in some regions than in others when certain lockdown measures came into force, based on measuring health outcomes relative to the region-specific start of the epidemic...

Université de Fribourg

Treatment Evaluation With Multiple Outcome Periods Under Endogeneity and Attrition

Fröhlich, Markus ; Huber, Martin

In: Journal of the American Statistical Association, 2014, vol. 109, no. 508, p. 1697-1711

This article develops a nonparametric methodology for treatment evaluation with multiple outcome periods under treatment endogeneity and missing outcomes. We use instrumental variables, pretreatment characteristics, and short-term (or intermediate) outcomes to identify the average treatment effect on the outcomes of compliers (the subpopulation whose treatment reacts on the instrument) in...

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 ...

Université de Fribourg

Including covariates in the regression discontinuity design

Frölich, Markus ; Huber, Martin

(Working Papers SES ; 489)

This paper proposes a fully nonparametric kernel method to account for observed covariates in regression discontinuity designs (RDD), which may increase precision of treatment effect estimation. It is shown that conditioning on covariates reduces the asymptotic variance and allows estimating the treatment effect at the rate of one-dimensional nonparametric regression, irrespective of the...