Discovering subgroups with the maximum average treatment effect is crucial for targeted decision making in domains such as precision medicine, public policy, and education. While most prior work is formulated in the potential outcome framework, the corresponding structural causal model (SCM) for this task has been largely overlooked. In practice, two approaches dominate. The first estimates pointwise conditional treatment effects and then fits a tree on those estimates, effectively turning subgroup estimation into the harder problem of accurate pointwise estimation. The second constructs decision trees or rule sets with ad-hoc 'causal' heuristics, typically without rigorous justification for why a given heuristic may be used or whether such heuristics are necessary at all. We address these issues by studying the problem directly under the SCM framework. Under the assumption of a partition-based model, we show that optimal subgroup discovery reduces to recovering the data-generating models and hence a standard supervised learning problem (regression or classification). This allows us to adopt any partition-based methods to learn the subgroup from data. We instantiate the approach with CART, arguably one of the most widely used tree-based methods, to learn the subgroup with maximum treatment effect. Finally, on a large collection of synthetic and semi-synthetic datasets, we compare our method against a wide range of baselines and find that our approach, which avoids such causal heuristics, more accurately identifies subgroups with maximum treatment effect. Our source code is available at https://github.com/ylincen/causal-subgroup.
Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis
Graduate School of Interdisciplinary Science and Engineering in Health Systems（交叉学科科学与工程健康系统研究生院）；Toshiba Corporation（东芝公司）；System AI Laboratory（系统人工智能实验室）；Faculty of Data Science（数据科学学院）
Causal Analysis of the TOPCAT Trial: Spironolactone for Preserved Cardiac Function Heart Failure
causaLens（causaLens公司）；King’s College London（伦敦国王学院）；Baylor Scott & White Research Institute（贝勒斯科特与怀特研究所）；University of Mississippi（密西西比大学）；Hobart Group（霍巴特集团）
Optimal Kernel Choice for Score Function-based Causal Discovery
The University of Melbourne（墨尔本大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of California, San Diego（加州大学圣迭戈分校）；Huazhong University of Science and Technology（华中科技大学）；The University of Sydney（悉尼大学）；Carnegie Mellon University（卡内基梅隆大学）
Department of Statistics, Korea University（高丽大学统计系）；Department of Statistics, Seoul National University（首尔大学统计系）；Department of Statistics and Data Science, Carnegie Mellon University（卡内基梅隆大学统计与数据科学系）
PRISM-Physics: Causal DAG-Based Process Evaluation for Physics Reasoning
Stanford University（斯坦福大学）；Tsinghua University（清华大学）；University of California Los Angeles（加州大学洛杉矶分校）；Harvard University（哈佛大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
Efficient Subgroup Analysis via Optimal Trees with Global Parameter Fusion
Division of Biostatistics, University of California, Berkeley（加州大学伯克利分校生物统计学系）；Helen Wills Neuroscience Institute, University of California, Berkeley（加州大学伯克利分校海伦·威尔斯神经科学研究所）
