  In personalised decision making, evidence is required to determine whether an
action (treatment) is suitable for an individual. Such evidence can be obtained
by modelling treatment effect heterogeneity in subgroups. The existing
interpretable modelling methods take a top-down approach to search for
subgroups with heterogeneous treatment effects and they may miss the most
specific and relevant context for an individual. In this paper, we design a
\emph{Treatment effect pattern (TEP)} to represent treatment effect
heterogeneity in data. To achieve an interpretable presentation of TEPs, we use
a local causal structure around the outcome to explicitly show how those
important variables are used in modelling. We also derive a formula for
unbiasedly estimating the \emph{Conditional Average Causal Effect (CATE)} using
the local structure in our problem setting. In the discovery process, we aim at
minimising heterogeneity within each subgroup represented by a pattern. We
propose a bottom-up search algorithm to discover the most specific patterns
fitting individual circumstances the best for personalised decision making.
Experiments show that the proposed method models treatment effect heterogeneity
better than three other existing tree based methods in synthetic and real world
data sets.
