Concise Summarization of Heterogeneous Treatment Effect Using Total   Variation Regularized Regression
  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. 
Alex Deng, Pengchuan Zhang, Shouyuan Chen, Dong Woo Kim, Jiannan Lu
stat.AP