This paper shows that robust inference under weak identification is important to the evaluation of many influential macro asset pricing models, including (time-varying) rare-disaster risk models and long-run risk models. Building on recent developments in the conditional inference literature, we provide a novel conditional specification test by simulating the critical value conditional on a sufficient statistic. This sufficient statistic can be intuitively interpreted as a measure capturing the macroeconomic information decoupled from the underlying content of asset pricing theories. Macro-finance decoupling is an effective way to improve the power of the specification test when asset pricing theories are difficult to refute because of a severe imbalance in the information content about the key model parameters between macroeconomic moment restrictions and asset pricing cross-equation restrictions. We apply the proposed conditional specification test to the evaluation of a time-varying rare-disaster risk model and the construction of robust model uncertainty sets.