Learning Latent Dynamical Causal Processes for Single-Cell Perturbation Prediction

ArXi:2605.25581v1 Announce Type: new Single-cell perturbation prediction aims to infer how cells respond to unseen interventions and to achieve out-of-distribution (OOD) generalization, providing a computational route to understanding how perturbations reshape cellular programs over time. Existing machine learning methods have made important progress, but typically capture only one side of the response. Latent causal approaches seek mechanisms that generalization and interpretation, yet often treat perturbation effects as static outcomes.