When Missingness Carries Meaning: Causal Federated Multi-View Clustering with Informative View Missingness

Federated multi-view clustering enables multiple institutions to cluster data collaboratively without sharing raw features, yet in practice many samples lack one or more views. Existing methods treat the missing pattern as a mere index: they use what is observed, impute what is not, and implicitly assume that views are missing at random. In practice, missingness often carries meaning, as which views a sample lacks is correlated with its latent semantics: clinicians typically order an imaging examination only when initial symptoms suggest severe disease, so a missing imaging view indicates a likely milder case. Ignoring this discards useful information and introduces selection bias, since each client observes only samples with an available view. We propose CFIMV, which places the missing mechanism inside a causal graph and builds three components on it: (i) a mechanism model that turns the missing pattern into additional evidence for cluster assignment and into inverse-probability weights that correct client-side selection bias; (ii) sample-level counterfactual view imputation, replacing cluster-level imputation that fills a missing view with the prototype of the currently assigned cluster and thus reinforces assignment errors; and (iii) a label-free reliability score and doubly robust prototypes that determine how much imputed views are trusted. Extensive experiments demonstrate the superiority of CFIMV against state-of-the-art baselines.