Before revision:  Interestingly our analysis suggests that data corrupted by adding noise to causal features will degrade out-of-domain performance, while noise added to non-causal features may make models more robust out-of-domain .
After revision:  Our analysis provides some insights that help to explain the efficacy of CAD. Moreover, we develop the hypothesis that while adding noise to causal features will degrade out-of-domain performance, while noise added to non-causal features may make models more robust out-of-domain .