  Heterogeneous treatment effect models allow us to compare treatments at
subgroup and individual levels, and are of increasing popularity in
applications like personalized medicine, advertising, and education. In this
talk, we first survey different causal estimands used in practice, which focus
on estimating the difference in conditional means. We then propose DINA, the
difference in natural parameters, to quantify heterogeneous treatment effect in
exponential families and the Cox model. For binary outcomes and survival times,
DINA is both convenient and more practical for modeling the influence of
covariates on the treatment effect. Second, we introduce a meta-algorithm for
DINA, which allows practitioners to use powerful off-the-shelf machine learning
tools for the estimation of nuisance functions, and which is also statistically
robust to errors in inaccurate nuisance function estimation. We demonstrate the
efficacy of our method combined with various machine learning base-learners on
simulated and real datasets.
