Causal effects are usually estimated under the assumption of no interference between individuals. This assumption means that the potential outcomes for one individual are unaffected by the treatments received by other individuals. In many situations, this is not reasonable to assume. Moreover,...

Semi-competing risks data frequently arise in biomedical studies when time to a disease landmark event is subject to dependent censoring by death, the observation of which however is not precluded by the occurrence of the landmark event. In observational studies, the analysis of such data can be...

In this paper we introduce the Random Recursive Partitioning (RRP) method. This method generates a proximity matrix which can be used in applications like average treatment effect estimation in observational studies. RRP is a Monte Carlo method that randomly generates non-empty recursive...

This paper provides a survey of six widely used non-experimental methods for estimating the impact of programmes in the context of developing economies (instrumental variables, regression discontinuity, direct matching, propensity score matching, linear r

Using a rich and unique combined administrative-survey dataset, this paper explores how sensitive propensity score (PS) matching estimates of Active Labor Market Policies (ALMPs) based on the selection-on-observables assumption are to typically unobserved covariates. Using a sample of German...

Large-scale environmental programs generally commit substantial societal resources, making the evaluation of their actual effects on the relevant outcomes imperative. As the example of the subsidization of energy-saving appliances illustrates, much of the applied environmental economics...

This paper provides a preliminary assessment of recent reforms of German employment promotion policy. While several recent studies analyze the impact of measures of employment promotion for the case of Germany, no comparable study exists on the aggregate level, thus precluding any assessment of...

A new matching method is proposed for the estimation of the average treatment effect of social policy interventions (e.g., training programs or health care measures. Given an outcome variable, a treatment and a set of pre-treatment covariates, the method is based on the examination of random...