Understanding treatment effect heterogeneity is vital for scientific and policy research. However, identifying and evaluating subgroup effects pose significant challenges due to the typically unknown subgroup structure. Recently, a novel approach, causal k-means clustering, has emerged to assess effect heterogeneity by applying the k-means algorithm to unknown counterfactual functions. In this paper, we expand upon this framework by integrating hierarchical and density-based clustering algorithms. We present plug-in estimators which are simple and readily implementable using off-the-shelf algorithms, and study their rate of convergence. Our findings significantly extend the capabilities of the causal clustering framework, thereby contributing to the progression of methodologies for identifying homogeneous subgroups in treatment response, consequently facilitating more nuanced and targeted interventions. The proposed methods also open up new avenues for clustering with generic pseudo-outcomes. We explore finite sample properties via simulation, and illustrate the proposed methods in voting and employment projection datasets.