  Estimation of conditional average treatment effects (CATEs) plays an
essential role in modern medicine by informing treatment decision-making at a
patient level. Several metalearners have been proposed recently to estimate
CATEs in an effective and flexible way by re-purposing predictive machine
learning models for causal estimation. In this chapter, we summarize the
literature on metalearners and provide concrete guidance for their application
for treatment heterogeneity estimation from randomized controlled trials' data
with survival outcomes. The guidance we provide is supported by a comprehensive
simulation study in which we vary the complexity of the underlying baseline
risk and CATE functions, the magnitude of the heterogeneity in the treatment
effect, the censoring mechanism, and the balance in treatment assignment. To
demonstrate the applicability of our findings, we reanalyze the data from the
Systolic Blood Pressure Intervention Trial (SPRINT) and the Action to Control
Cardiovascular Risk in Diabetes (ACCORD) study. While recent literature reports
the existence of heterogeneous effects of intensive blood pressure treatment
with multiple treatment effect modifiers, our results suggest that many of
these modifiers may be spurious discoveries. This chapter is accompanied by
survlearners, an R package that provides well-documented implementations of the
CATE estimation strategies described in this work, to allow easy use of our
recommendations as well as the reproduction of our numerical study.
