CM-CaFE: A Clustering Method with Causality-based Feature Embedding

What is it about?

Our research focuses on improving data grouping techniques (clustering) to enable computers to more accurately identify natural patterns within data. Traditional methods rely heavily on superficial correlations between features; however, many observed correlations can be misleading. For example, ice cream sales and drowning incidents appear correlated, yet both are actually influenced by seasonal factors. Such spurious correlations can result in inaccurate and difficult-to-interpret clustering outcomes. To address this issue, we propose a novel method called CM-CaFE, which optimizes data representation by uncovering genuine causal relationships among features. Analogous to analyzing seasonal factors to explain the true relationship between ice cream consumption and drowning, our approach leverages causal relationship networks to eliminate false associations, thereby constructing a more reliable feature-relationship graph. By integrating causal relationship learning with clustering optimization, our method not only achieves more precise data partitioning but also provides interpretability regarding the underlying feature relationships. Experimental results demonstrate that CM-CaFE outperforms existing techniques across various data types, exhibiting significant advantages especially when handling complex associative data. Thus, our approach offers a more trustworthy tool for applications such as medical subtyping and user profiling.

Featured Image

Photo by Alfons Morales on Unsplash

Photo by Alfons Morales on Unsplash

Why is it important?

This study addresses a critical limitation in conventional clustering methods by introducing the Clustering Method with Causal Feature Embedding (CM-CaFE), a novel framework that integrates causal learning to enhance clustering performance and feature interpretability. Existing feature representation approaches rely on correlation-based metrics, which are prone to spurious correlations—erroneously associating features without causal relationships—thereby undermining clustering accuracy and interpretability. CM-CaFE uniquely mitigates this issue by leveraging causal inference to identify discriminative feature embeddings. Specifically, the method first constructs an undirected causal graph via state-of-the-art Markov blanket learning, extracts maximal fully-connected causal subgraphs, and merges them to derive a causal matrix. A joint optimization objective, combining clustering loss and causal matrix fitting, is then formulated to learn a causal transformation matrix that maps raw data into a causally informed embedding space. Experimental validation across diverse datasets demonstrates CM-CaFE’s superiority over existing methods in both clustering efficacy and interpretability. This work is timely, as it aligns with growing interest in causality-driven machine learning, offering a principled solution to disentangle spurious correlations—a pervasive challenge in data-driven sciences. By bridging causal reasoning with unsupervised learning, CM-CaFE advances the design of robust clustering frameworks with applications in domains requiring transparent and reliable feature analysis.