  The estimation of heterogeneous treatment effects (HTEs) has attracted
considerable interest in many disciplines, most prominently in medicine and
economics. Contemporary research has so far primarily focused on continuous and
binary responses where HTEs are traditionally estimated by a linear model,
which allows the estimation of constant or heterogeneous effects even under
certain model misspecifications. More complex models for survival, count, or
ordinal outcomes require stricter assumptions to reliably estimate the
treatment effect. Most importantly, the noncollapsibility issue necessitates
the joint estimation of treatment and prognostic effects. Model-based forests
allow simultaneous estimation of covariate-dependent treatment and prognostic
effects, but only for randomized trials. In this paper, we propose
modifications to model-based forests to address the confounding issue in
observational data. In particular, we evaluate an orthogonalization strategy
originally proposed by Robinson (1988, Econometrica) in the context of
model-based forests targeting HTE estimation in generalized linear models and
transformation models. We found that this strategy reduces confounding effects
in a simulated study with various outcome distributions. We demonstrate the
practical aspects of HTE estimation for survival and ordinal outcomes by an
assessment of the potentially heterogeneous effect of Riluzole on the progress
of Amyotrophic Lateral Sclerosis.
