  In this extended abstract paper, we address the problem of interpretability
and targeted regularization in causal machine learning models. In particular,
we focus on the problem of estimating individual causal/treatment effects under
observed confounders, which can be controlled for and moderate the effect of
the treatment on the outcome of interest. Black-box ML models adjusted for the
causal setting perform generally well in this task, but they lack interpretable
output identifying the main drivers of treatment heterogeneity and their
functional relationship. We propose a novel deep counterfactual learning
architecture for estimating individual treatment effects that can
simultaneously: i) convey targeted regularization on, and produce quantify
uncertainty around the quantity of interest (i.e., the Conditional Average
Treatment Effect); ii) disentangle baseline prognostic and moderating effects
of the covariates and output interpretable score functions describing their
relationship with the outcome. Finally, we demonstrate the use of the method
via a simple simulated experiment.
