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...

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...

We investigate the finite sample performance of causal machine learning estimators for heterogeneous causal effects at different aggregation levels. We employ an Empirical Monte Carlo Study that relies on arguably realistic data generation processes (DGPs) based on actual data. We consider 24...

Linear Econometric Models with Machine Learning -- Nonlinear Econometric Models with Machine Learning -- The Use of Machine Learning in Treatment Effect Estimation.-Forecasting with Machine Learning Methods.-Causal Estimation of Treatment Effects From Observational Health Care Data Using Machine...

We investigate the finite sample performance of causal machine learning estimators for heterogeneous causal effects at different aggregation levels. We employ an Empirical Monte Carlo Study that relies on arguably realistic data generation processes (DGPs) based on actual data. We consider 24...

In this paper, we consider the visualization and statistical modeling of financial data (e.g., sales, assets) for many global firms which are listed and delisted. This study presents an exploratory data analysis carried out in the R programming language. The results show that a log-linear model...