  We study the problem of inferring heterogeneous treatment effects from
time-to-event data. While both the related problems of (i) estimating treatment
effects for binary or continuous outcomes and (ii) predicting survival outcomes
have been well studied in the recent machine learning literature, their
combination -- albeit of high practical relevance -- has received considerably
less attention. With the ultimate goal of reliably estimating the effects of
treatments on instantaneous risk and survival probabilities, we focus on the
problem of learning (discrete-time) treatment-specific conditional hazard
functions. We find that unique challenges arise in this context due to a
variety of covariate shift issues that go beyond a mere combination of
well-studied confounding and censoring biases. We theoretically analyse their
effects by adapting recent generalization bounds from domain adaptation and
treatment effect estimation to our setting and discuss implications for model
design. We use the resulting insights to propose a novel deep learning method
for treatment-specific hazard estimation based on balancing representations. We
investigate performance across a range of experimental settings and empirically
confirm that our method outperforms baselines by addressing covariate shifts
from various sources.
