Search papers, labs, and topics across Lattice.
This paper introduces a novel method for structure learning on clustered data by extending classical mixed models to accommodate local cluster-level effects while estimating a global structure. The approach utilizes a differentiable graph coupling mechanism to ensure acyclicity in the combined fixed- and random-effects graphs. Experimental results demonstrate that this method identifies dependencies overlooked by existing estimators, highlighting its effectiveness in complex, heterogeneous datasets.
By integrating cluster-specific effects into causal structure learning, this method uncovers hidden dependencies that traditional approaches miss.
Recent algorithmic advances have made directed acyclic graph (DAG) structure learning scalable for causal discovery. Yet, the currently available techniques assume a completely homogeneous population, precluding their application to clustered data where cluster-specific variations (e.g., patient-specific effects) are common. We address this issue by introducing a new approach that estimates a global structure while accounting for local cluster-level effects. The key idea is to extend the fixed- and random-effects framework of classical mixed models to the structure learning setting. Towards this end, we present a differentiable graph coupling mechanism that guarantees the union of the fixed- and random-effects graphs remains acyclic. Computationally, we provide a provably convergent first-order method and leverage efficient batched updates across clusters. Statistically, we establish identifiability of the model and show that our approach recovers the true structure asymptotically. In experiments on real and synthetic data, our proposal detects dependencies missed by alternative estimators, underscoring its value for structure learning in clustered settings.
