This paper examines the econometric causal model and the interpretation of empirical evidence based on thought experiments that was developed by Ragnar Frisch and Trygve Haavelmo. We compare the econometric causal model with two currently popular causal frameworks: the Neyman-Rubin causal model...

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...

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...

Much has been said about the stylized fact that the economically successful are not only wealthier but also healthier than the less affluent. There is little doubt about the existence of this socio-economic gradient in health, but there remains a vivid debate about its source. In this paper, we...

The UK National Audit Office (NAO) scrutinises public spending on behalf of Parliament, helping it to hold government departments to account and helping public bodies improve performance and delivery. We publish around 60 value for money studies each year across a range of government activities,...

This paper reviews methods that seek to draw causal inference from non-experimental data and shows how they can be applied to undertake ex-post evaluation of transport interventions. In particular, the paper discusses the underlying principles of techniques for treatment effect estimation with...

Evaluation studies aim to provide answers to important questions like: How does this program or policy intervention affect the outcome variables of interest? In order to answer such questions, using the traditional statistical evaluation (or causal inference) methods, some conditions must be...

We investigate the behaviour of the Lasso for selecting invalid instruments in linear instrumental variables models for estimating causal effects of exposures on outcomes, as proposed recently by Kang, Zhang, Cai and Small (2016, Journal of the American Statistical Association). Invalid...