Statistics paper proves you don't need to fix your broken model — just use the right loss function

A mathematics paper shows that when your statistical model is fundamentally wrong, you can still make optimal decisions by choosing the right loss function instead of fixing the model itself. In practice, this means practitioners should stop trying to patch broken assumptions and instead use maximum likelihood estimation or efficient method-of-moments estimators, which will work fine anyway.

For decades, statisticians have assumed that if your model is misspecified — if your assumptions about the data are wrong — you need to either fix the model or accept worse decisions. This paper says that's not true; the math works out so that certain estimation methods produce optimal decisions regardless of misspecification. The practical implication is blunt: stop spending effort on model repair. Use maximum likelihood or two-step GMM, and you'll get the same answer as if your model were correct.