This paper discusses pairing double/debiased machine learning (DDML) with stacking, a model averaging method for combining multiple candidate learners, to estimate structural parameters. We introduce two new stacking approaches for DDML: short-stacking exploits the cross-fitting step of DDML to...

The Productive Safety Net Programme (PSNP) is Ethiopia's poverty reduction strategy that forms the most important pillar of the country's agricultural transformation into a more productive and competitive sector. However, the extent to which the PSNP is linked to agriculture is unclear. This...

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

Ancillary experiments' are a new technique whereby researchers use a completed experiment conducted by others to recover causal estimates of a randomized intervention on new outcomes. The method requires pairing new outcome data with randomized treatments the researchers themselves did not...

This article develops a theoretical model for evaluating mandatory activation of welfare recipients in complex activation programmes. The model aims to summarize and describe heterogeneous content that is difficult to comprehend because of local variations, staff characteristics, or other...

This paper proposes a score-driven model for filtering time-varying causal parameters through the use of instrumental variables. In the presence of suitable instruments, we show that we can uncover dynamic causal relations between variables, even in the presence of regressor endogeneity which...

Critics protest loudly against restrictions imposed by politicians during the COVID-19 pandemic in Germany: Mandatory masks, lockdowns, school and business closures. This paper examines (1) the extent to which these policies have indirectly contributed to limiting the number of COVID-19 cases...

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 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...

Individuals’ socioeconomic status (SES) is positively correlated with their health status. While the existence of this gradient may be uncontroversial, the same cannot be said about its explanation. In this paper, we extend the approach of testing for the absence of causal channels developed...