1 research outputs found
Learning Invariant Representations with Missing Data
Spurious correlations allow flexible models to predict well during training
but poorly on related test distributions. Recent work has shown that models
that satisfy particular independencies involving correlation-inducing
\textit{nuisance} variables have guarantees on their test performance.
Enforcing such independencies requires nuisances to be observed during
training. However, nuisances, such as demographics or image background labels,
are often missing. Enforcing independence on just the observed data does not
imply independence on the entire population. Here we derive \acrshort{mmd}
estimators used for invariance objectives under missing nuisances. On
simulations and clinical data, optimizing through these estimates achieves test
performance similar to using estimators that make use of the full data.Comment: CLeaR (Causal Learning and Reasoning) 202