 Universal causality is a mathematical framework based on higher-order category theory, which generalizes previous approaches based on directed graphs and regular categories. It models causal interventions as a higher-order category over simplicial sets and objects, where morphisms are order-preserving injections and subjections over finite-ordered sets. Non-random interventions on causal structures are modeled as space operators that map n-simplices into lower-level simplices. Causal models are defined as a category, for example defining the schema of a relational causal model or a symmetric-monoidal category representation of DAG models. Data instances are mapped functorially into a set of instances using the category of sets and functions between sets. A growth-indeed category of elements is induced by the yinitilema, enabling combining formal causal models with data instances. Causal inference between layers is defined as a lifting problem, a commutative diagram whose objects are categories, and whose morphisms are functors that are characterized as different types of vibrations. This article was authored by Shridhar Mahadevan. We are article.tv, links in the description below.