Essays on Methods for Causal Inference

Abstract

This dissertation consists of three papers sharing the objective to analyze how machine learning methods can be useful to economists and econometricians in their pursuit to understand causal mechanisms operating in the economy. Such causal knowledge is essential when designing policies that help achieve societal goals. ML techniques are increasingly applied in and adapted to practical policy settings. These are characterized by the same type of endogeneity problems that make actionable inference from data difficult and that economists are occupied with. Thus, there are many potential synergies between ML and economics that are surfacing on both the academic and policy-making agendas. Contributions to two points of interchange between the two fields are made. First, ML can be used to improve or extend widely-used identification techniques in economics and, second, insights into causal modeling from the ML community can be introduced as novel routes to identification in economics. The first paper of this dissertation falls in the former, the second and third paper in the latter category.