Salle 3001

Christophe Bruneel-Zupanc (KU Leuven) “Don’t (fully) exclude me, it’s not necessary! Causal inference with semi-IVs”

Abstract: This paper proposes semi-instrumental variables (semi-IVs) as a practical alternative to instrumental variables (IVs) to identify the causal effect of a binary (or discrete) endogenous treatment. A semi-IV is a less restrictive form of instrument that is relevant but excluded only from one, not necessarily both, potential outcomes. Having two semi-IVs, one excluded from the treated and the other from the untreated potential outcomes, is sufficient to nonparametrically point identify the local average treatment effect (LATE) and marginal treatment effect (MTE). In practice, semi-IVs provide a solution to the problem of finding valid IVs, because they are easier to find: most selection-specific shocks, policies, costs, or benefits are valid semi-IVs. As an application, I estimate the returns to manufacturing using sector-specific semi-IVs.