  Policy learning is an important component of many real-world learning
systems. A major challenge in policy learning is how to adapt efficiently to
unseen environments or tasks. Recently, it has been suggested to exploit
invariant conditional distributions to learn models that generalize better to
unseen environments. However, assuming invariance of entire conditional
distributions (which we call full invariance) may be too strong of an
assumption in practice. In this paper, we introduce a relaxation of full
invariance called effect-invariance (e-invariance for short) and prove that it
is sufficient, under suitable assumptions, for zero-shot policy generalization.
We also discuss an extension that exploits e-invariance when we have a small
sample from the test environment, enabling few-shot policy generalization. Our
work does not assume an underlying causal graph or that the data are generated
by a structural causal model; instead, we develop testing procedures to test
e-invariance directly from data. We present empirical results using simulated
data and a mobile health intervention dataset to demonstrate the effectiveness
of our approach.
