How to do statistical inference under model misspecification?

Most statistical methods assume a correctly specified model. Real data rarely match any specific model exactly. Robust methods, semiparametric inference and sandwich estimators partially address this, but no unified framework exists.

Open questions include how to report uncertainty when even the model space is uncertain.