  Randomized controlled experiment has long been accepted as the golden
standard for establishing causal link and estimating causal effect in various
scientific fields. Average treatment effect is often used to summarize the
effect estimation, even though treatment effects are commonly believed to be
varying among individuals. In the recent decade with the availability of "big
data", more and more experiments have large sample size and increasingly rich
side information that enable and require experimenters to discover and
understand heterogeneous treatment effect (HTE). There are two aspects in HTE
understanding, one is to predict the effect conditioned on a given set of side
information or a given individual, the other is to interpret the HTE structure
and summarize it in a memorable way. The former aspect can be treated as a
regression problem, and the latter aspect focuses on concise summarization and
interpretation. In this paper we propose a method that can achieve both at the
same time. This method can be formulated as a convex optimization problem, for
which we provide stable and scalable implementation.
