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In this paper we present a data-adaptive estimation procedure for estimationof average treatment effects in a time-to-event setting based on generalizedrandom forests. In these kinds of settings, the definition of causal effectparameters are complicated by competing risks; here we distinguish betweentreatment effects on the crude and the net probabilities, respectively. Tohandle right-censoring, and to switch between crude and net probabilities, wepropose a two-step procedure for estimation, applying inverse probabilityweighting to construct time-point specific weighted outcomes as input for theforest. The forest adaptively handles confounding of the treatment assigned byapplying a splitting rule that targets a causal parameter. We demonstrate thatour method is effective for a causal search through a list of treatments to beranked according to the magnitude of their effect. We further apply our methodto a dataset from the Danish health registries where it is of interest todiscover drugs with an unexpected protective effect against relapse of severedepression.

Ranking of average treatment effects with generalized random forests for time-to-event outcomes