Tuesday  11 March

Causal exageration: unconfounded but inflated causal estimates

Vincent Bagilet – ENS (post-doc)

Abstract:

The credibility revolution in economics has made causal inference methods ubiquitous. Simultaneously, an increasing amount of evidence highlights that the literature strongly favors statistically significant results. I show that these two phenomena interact in a way that can substantially worsen the reliability of published estimates: while causal identification strategies alleviate bias caused by confounders, they reduce statistical power and can create another type of bias—exaggeration— when combined with selection on significance. This is consequential in fields such as environmental economics, as estimates turn into decision-making parameters for policy makers conducting cost/benefit analyses. I characterize this confounding-exaggeration trade-off theoretically and using realistic Monte Carlo simulations replicating prevailing identification strategies and document it in an example literature. I then discuss potential avenues to address this issue.

Registration, please contact robin@em-lyon.com

Room A2-113, Lyon campus