  A central question in many fields of scientific research is to determine how
an outcome would be affected by an action, or to measure the effect of an
action (a.k.a treatment effect). In recent years, a need for estimating the
heterogeneous treatment effects conditioning on the different characteristics
of individuals has emerged from research fields such as personalized
healthcare, social science, and online marketing. To meet the need, researchers
and practitioners from different communities have developed algorithms by
taking the treatment effect heterogeneity modeling approach and the uplift
modeling approach, respectively. In this paper, we provide a unified survey of
these two seemingly disconnected yet closely related approaches under the
potential outcome framework. We then provide a structured survey of existing
methods by emphasizing on their inherent connections with a set of unified
notations to make comparisons of the different methods easy. We then review the
main applications of the surveyed methods in personalized marketing,
personalized medicine, and social studies. Finally, we summarize the existing
software packages and present discussions based on the use of methods on
synthetic, semi-synthetic and real world data sets and provide some general
guidelines for choosing methods.
