  In many experimental or quasi-experimental studies, outcomes of interest are
only observed for subjects who select (or are selected) to engage in the
activity generating the outcome. Outcome data is thus endogenously missing for
units who do not engage, in which case random or conditionally random treatment
assignment prior to such choices is insufficient to point identify treatment
effects. Non-parametric partial identification bounds are a way to address
endogenous missingness without having to make disputable parametric
assumptions. Basic bounding approaches often yield bounds that are very wide
and therefore minimally informative. We present methods for narrowing
non-parametric bounds on treatment effects by adjusting for potentially large
numbers of covariates, working with generalized random forests. Our approach
allows for agnosticism about the data-generating process and honest inference.
We use a simulation study and two replication exercises to demonstrate the
benefits of our approach.
