  While machine learning is rapidly being developed and deployed in health
settings such as influenza prediction, there are critical challenges in using
data from one environment in another due to variability in features; even
within disease labels there can be differences (e.g. "fever" may mean something
different reported in a doctor's office versus in an online app). Moreover,
models are often built on passive, observational data which contain different
distributions of population subgroups (e.g. men or women). Thus, there are two
forms of instability between environments in this observational transport
problem. We first harness knowledge from health to conceptualize the underlying
causal structure of this problem in a health outcome prediction task. Based on
sources of stability in the model, we posit that for human-sourced data and
health prediction tasks we can combine environment and population information
in a novel population-aware hierarchical Bayesian domain adaptation framework
that harnesses multiple invariant components through population attributes when
needed. We study the conditions under which invariant learning fails, leading
to reliance on the environment-specific attributes. Experimental results for an
influenza prediction task on four datasets gathered from different contexts
show the model can improve prediction in the case of largely unlabelled target
data from a new environment and different constituent population, by harnessing
both environment and population invariant information. This work represents a
novel, principled way to address a critical challenge by blending domain
(health) knowledge and algorithmic innovation. The proposed approach will have
a significant impact in many social settings wherein who and where the data
comes from matters.
