  In this paper we present a data-adaptive estimation procedure for estimation
of average treatment effects in a time-to-event setting based on generalized
random forests. In these kinds of settings, the definition of causal effect
parameters are complicated by competing risks; here we distinguish between
treatment effects on the crude and the net probabilities, respectively. To
handle right-censoring, and to switch between crude and net probabilities, we
propose a two-step procedure for estimation, applying inverse probability
weighting to construct time-point specific weighted outcomes as input for the
forest. The forest adaptively handles confounding of the treatment assigned by
applying a splitting rule that targets a causal parameter. We demonstrate that
our method is effective for a causal search through a list of treatments to be
ranked according to the magnitude of their effect. We further apply our method
to a dataset from the Danish health registries where it is of interest to
discover drugs with an unexpected protective effect against relapse of severe
depression.
