Abstract Body (Do not enter title and authors here): Postnatal development requires precise coordination across molecular, cellular and tissue scales. However, existing methods often fail to resolve the causal hierarchies that underlie this coordination. Transcriptome-wide studies can identify trait-associated genes but cannot predict how gene expression changes propagate through gene–cell–tissue networks. Here, we present CausaLink, a cross-scale causal mapping framework that reconstructs directed networks by integrating transcriptomic and tissue biomechanical data. We applied it to the postnatal pulmonary arterial tissue remodeling, where a surge in arterial flow triggers conversion of low-flow conduits into high-flow conduits. Longitudinal single-cell and bulk transcriptomic profiling of proximal pulmonary artery (P2-P84) mapped dynamic vascular lineage transitions. Our analysis suggested a bifurcation of shared fibroblast–smooth muscle progenitor in the neonatal phase, initiating downstream shifts at both the cellular and tissue levels. In this context, CausaLink identified Mgl2⁺ macrophages as central regulators of balanced artery growth. Network-based, multi-trait simulation predicted Mgl2⁺ macrophages promote lumen expansion while preventing pathological wall thinning or thickening. We validated these predictions in a human induced pluripotent stem cell-derived arterial assembloid populated with MGL^high macrophages. In this model, MGL^high macrophages drove fibroblast expansion and coordinated tissue growth, as predicted. CausaLink enables systemic prediction across biological scales and offers a data-driven approach for therapeutic design in diseases involving disrupted tissue homeostasis.