We investigate the effectiveness of three different job-search and training programmes for German long-term unemployed persons. On the basis of an extensive administrative data set, we evaluated the effects of those programmes on various levels of aggregation using Causal Machine Learning. We...

We investigate heterogenous employment effects of Flemish training programmes. Based on administrative individual data, we analyse programme effects at various aggregation levels using Modified Causal Forests (MCF), a causal machine learning estimator for multiple programmes. While all...

This paper investigates the regional differences in the spread of COVID-19 infections in Germany. A machine learning selection procedure is used to reduce variables from a pool of potential influencing variables. The empirical analysis shows that both regional structural variables and regionally...

Many targeted childhood interventions such as the Perry Preschool Project select eligible children based on a risk score. The variables entering the risk score and their corresponding weights are usually chosen ad hoc and are unlikely to be optimal. This paper develops a simple economic model...

Matching-type estimators using the propensity score are the major workhorse in active labour market policy evaluation. This work investigates if machine learning algorithms for estimating the propensity score lead to more credible estimation of average treatment effects on the treated using a...

Inequality is a dynamic phenomenon, and the relative and absolute positions of individuals are subject to frequent shocks. It is important to know if preventive interventions mitigate adverse inequality effects of labor market shocks. We consider individuals up to three months before the...

This paper focuses on the spatial variation in the uptake of social security benefits following a large and detrimental exogenous shock. Specifically, we focus on the Global Financial Crisis (GFC) and the onset of the COVID-19 pandemic. We construct a two-period panel of 66 Territorial...

Binary treatments are often ex-post aggregates of multiple treatments or can be disaggregated into multiple treatment versions. Thus, effects can be heterogeneous due to either effect or treatment heterogeneity. We propose a decomposition method that uncovers masked heterogeneity, avoids...

We provide new insights regarding the finding that Medicaid increased emergency department (ED) use from the Oregon experiment. We find meaningful heterogeneous impacts of Medicaid on ED use using causal machine learning methods. The treatment effect distribution is widely dispersed, and the...

Breakthroughs and backlashes have marked progress in the development and diffusion of Artificial Intelligence (AI). These shocks make the investment in developing an Artificial General Intelligence (AGI) subject to considerable uncertainty. This paper applies a real options model, extended to...