  Forest-based methods have recently gained in popularity for non-parametric
treatment effect estimation. Building on this line of work, we introduce causal
survival forests, which can be used to estimate heterogeneous treatment effects
in a survival and observational setting where outcomes may be right-censored.
Our approach relies on orthogonal estimating equations to robustly adjust for
both censoring and selection effects under unconfoundedness. In our
experiments, we find our approach to perform well relative to a number of
baselines.
