Purpose Nonexperimental research, defined as any kind of quantitative or qualitative research that is not an experiment, is the predominate kind of research design used in the social sciences. How to unambiguously and correctly present the results of nonexperimental research, however, remains...

An important goal when analyzing the causal effect of a treatment on an outcome is to understand the mechanisms through which the treatment causally works. We define a causal mechanism effect of a treatment and the causal effect net of that mechanism using the potential outcomes framework. These...

This paper focus on the determinants of poverty dynamics in Tunisia by performing a two-stage endogenous model using a Bayesian algorithm to avoid missing data. Based on this approach author show that panel data analysis can be performed through potential variables such presented by the work of...

This thesis examines causal inference related topics involving intermediate variables, and uses Bayesian methodologies to advance analysis capabilities in these areas. First, joint modeling of outcome variables with intermediate variables is considered in the context of birthweight and censored...

Customer scoring models are the core of scalable direct marketing. Uplift models provide an estimate of the incremental benefit from a treatment that is used for operational decision-making. Training and monitoring of uplift models require experimental data. However, the collection of data under...

The paper proposes an estimator to make inference of heterogeneous treatment effects sorted by impact groups (GATES) for non-randomised experiments. Observational studies are standard in policy evaluation from labour markets, educational surveys and other empirical studies. To control for a...

We investigate the finite sample performance of sample splitting, cross-fitting and averaging for the estimation of the conditional average treatment effect. Recently proposed methods, so-called meta- learners, make use of machine learning to estimate different nuisance functions and hence allow...

We use the US presidential election on 3 November 2020 to examine how the US president influences economic expectations of international experts. We design a large-scale RCT among 843 experts working in 107 countries, asking about their expectations regarding GDP growth, unemployment, inflation,...

For treatment effects - one of the core issues in modern econometric analysis - prediction and estimation are flip-sides of the same coin. As it turns out, machine learning methods are the tool for generalized prediction models. Combined with econometric theory allows us to estimate not only the...

We use the US presidential election on 3 November 2020 to examine how the US president influences economic expectations of international experts. We design a large-scale RCT among 843 experts working in 107 countries, asking about their expectations regarding GDP growth, unemployment, inflation,...