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.
翻译:在个人决策中,需要证据来确定一项行动(治疗)是否适合个人。这种证据可以通过在分组中模拟处理效果异同性来获得。现有的可解释的建模方法采取自上而下的方法来寻找具有不同处理效果的分组,它们可能错失个人最具体和相关的背景。在本文件中,我们设计了一个\emph{治疗效应模式(TEP)来代表数据中的处理效应异异性。为了实现可解释的TEP的展示,我们使用结果周围的当地因果结构来明确显示这些重要变量是如何在模拟中使用的。我们还利用我们问题设置中的当地结构,为无偏倚地估计\emph{Conditional Properal Causal effective (CATE) 提出了一个公式。在发现过程中,我们的目标是在以某种模式代表的每个分组中尽量减少异异性。我们提出一个自下而上而起的搜索算法,以发现最符合个人化决策的最佳环境的最具体模式。实验表明,拟议的方法模型处理异性效果比其他三种基于合成树的方法更好。