  Estimating heterogeneous treatment effects is important to tailor treatments
to those individuals who would most likely benefit. However, conditional
average treatment effect predictors may often be trained on one population but
possibly deployed on different, possibly unknown populations. We use
methodology for learning multi-accurate predictors to post-process CATE
T-learners (differenced regressions) to become robust to unknown covariate
shifts at the time of deployment. The method works in general for
pseudo-outcome regression, such as the DR-learner. We show how this approach
can combine (large) confounded observational and (smaller) randomized datasets
by learning a confounded predictor from the observational dataset, and auditing
for multi-accuracy on the randomized controlled trial. We show improvements in
bias and mean squared error in simulations with increasingly larger covariate
shift, and on a semi-synthetic case study of a parallel large observational
study and smaller randomized controlled experiment. Overall, we establish a
connection between methods developed for multi-distribution learning and
achieve appealing desiderata (e.g. external validity) in causal inference and
machine learning.
