  Shifts in environment between development and deployment cause classical
supervised learning to produce models that fail to generalize well to new
target distributions. Recently, many solutions which find invariant predictive
distributions have been developed. Among these, graph-based approaches do not
require data from the target environment and can capture more stable
information than alternative methods which find stable feature sets. However,
these approaches assume that the data generating process is known in the form
of a full causal graph, which is generally not the case. In this paper, we
propose I-SPEC, an end-to-end framework that addresses this shortcoming by
using data to learn a partial ancestral graph (PAG). Using the PAG we develop
an algorithm that determines an interventional distribution that is stable to
the declared shifts; this subsumes existing approaches which find stable
feature sets that are less accurate. We apply I-SPEC to a mortality prediction
problem to show it can learn a model that is robust to shifts without needing
upfront knowledge of the full causal DAG.
