Causal K-Means Clustering

arXiv:2405.03083v1 Announce Type: cross
Abstract: 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: Causal k-Means Clustering, which harnesses the widely-used k-means clustering algorithm to uncover the unknown subgroup structure. Our problem differs significantly from the conventional clustering setup …

abstract arxiv causal clustering cs.lg effects identify k-means population solution stat.me stat.ml subgroups treatment type