Inférence causale pour l'évaluation ex post des interventions dans les transports

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 non-randomly assigned treatments. The aim of these techniques is to quantify changes that have occurred due to explicit intervention (or 'treatment'). The paper argues that transport interventions are typically characterized by non-random assignment and that the key issues for successful ex-post evaluation involve identifying and adjusting for confounding factors. In contrast to conventional approaches for ex-ante appraisal, a major advantage of the statistical causal methods is that they can be applied without making strong a-priori theoretical assumptions. The paper provides empirical examples of the use of causal techniques to evaluate road network capacity expansions in US cities and High Speed Rail investments in Spain.