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）
Causal effects are often characterized with population summaries. These might provide an incomplete picture when there are heterogeneous treatment effects across subgroups. Since the subgroup structure is typically unknown, it is more challenging to identify and evaluate subgroup effects than population effects. We propose a new solution to this problem: \emph{Causal k-Means Clustering}, which leverages the k-means clustering algorithm to uncover the unknown subgroup structure. Our problem differs significantly from the conventional clustering setup since the variables to be clustered are unknown counterfactual functions. We present a plug-in estimator which is simple and readily implementable using off-the-shelf algorithms, and study its rate of convergence. We also develop a new bias-corrected estimator based on nonparametric efficiency theory and double machine learning, and show that this estimator achieves fast root-n rates and asymptotic normality in large nonparametric models. Our proposed methods are especially useful for modern outcome-wide studies with multiple treatment levels. Further, our framework is extensible to clustering with generic pseudo-outcomes, such as partially observed outcomes or otherwise unknown functions. Finally, we explore finite sample properties via simulation, and illustrate the proposed methods using a study of mobile-supported self-management for chronic low back pain.
A Unified Framework for Structure-Aware Clustering and Heterogeneous Causal Graph Learning
Department of Industrial and Systems Engineering, University of Florida（佛罗里达大学工业与系统工程系）；Department of Biostatistics, MD Anderson Cancer Center（MD安德森癌症中心生物统计学系）
Relaxing partition admissibility in Cluster-DAGs: a causal calculus with arbitrary variable clustering
Univ Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）；LIG（格勒诺布尔信息实验室）；University of Münster（明斯特大学）；Institute of Medical Informatics（医学信息学研究所）；Université de Lorraine（洛林大学）；CRAN（自动控制研究中心）
