Learning Invariant Representations with Missing Data.

Spurious correlations allow flexible models to predict well during training but poorly on related test populations. Recent work has shown that models that satisfy particular independencies involving correlation-inducing variables have guarantees on their test performance. Enforcing such independencies requires nuisances to be observed during training.