  Heterogeneous treatment effects are driven by treatment effect modifiers,
pre-treatment covariates that modify the effect of a treatment on an outcome.
Current approaches for uncovering these variables are limited to
low-dimensional data, data with weakly correlated covariates, or data generated
according to parametric processes. We resolve these issues by developing a
framework for defining model-agnostic treatment effect modifier variable
importance parameters applicable to high-dimensional data with arbitrary
correlation structure, deriving one-step, estimating equation and targeted
maximum likelihood estimators of these parameters, and establishing these
estimators' asymptotic properties. This framework is showcased by defining
variable importance parameters for data-generating processes with continuous,
binary, and time-to-event outcomes with binary treatments, and deriving
accompanying multiply-robust and asymptotically linear estimators. Simulation
experiments demonstrate that these estimators' asymptotic guarantees are
approximately achieved in realistic sample sizes for observational and
randomized studies alike. This framework is applied to gene expression data
collected for a clinical trial assessing the effect of a monoclonal antibody
therapy on disease-free survival in breast cancer patients. Genes predicted to
have the greatest potential for treatment effect modification have previously
been linked to breast cancer. An open-source R package implementing this
methodology, unihtee, is made available on GitHub at
https://github.com/insightsengineering/unihtee.
