Importantly,thesuccess ofanymitigation strategystrongly depends on the structure of the shift. Despite this, there has been little discussion of how toempirically assess the structure ofadistribution shift that one isencountering in practice. In this work, we adopt a causal framing to motivate conditional independence tests as akeytool for characterizing distribution shifts. Using our approach in two medical applications, we show that this knowledge can help diagnose failures offairness transfer,including cases where real-world shifts are more complexthanisoften assumed intheliterature.

Weight optimum andavoids difference consists latent-space.