  While developments in machine learning led to impressive performance gains on
big data, many human subjects data are, in actuality, small and sparsely
labeled. Existing methods applied to such data often do not easily generalize
to out-of-sample subjects. Instead, models must make predictions on test data
that may be drawn from a different distribution, a problem known as
\textit{zero-shot learning}. To address this challenge, we develop an
end-to-end framework using a meta-learning approach, which enables the model to
rapidly adapt to a new prediction task with limited training data for
out-of-sample test data. We use three real-world small-scale human subjects
datasets (two randomized control studies and one observational study), for
which we predict treatment outcomes for held-out treatment groups. Our model
learns the latent treatment effects of each intervention and, by design, can
naturally handle multi-task predictions. We show that our model performs the
best holistically for each held-out group and especially when the test group is
distinctly different from the training group. Our model has implications for
improved generalization of small-size human studies to the wider population.
