How to Modeling Real Causality for Real AI? Causality is represented - Pinaki Laskar on LinkedIn

How to Modeling Real Causality for Real AI? Causality is represented mathematically via Structural Causal Models (SCMs), with two key elements, a graph and a set of equations. The golden standard causal graph, C E, R, is a Bidirected Cyclic Multi-Graph or Causal Loop-Graph Network (BCG/CGN), where entity-vertices E (circles, nodes, points) in a causal BCG represent variables and edges R (arrows, links, ties, arcs, lines) represent causation, direct or inverse. It is strongly connected containing a directed path from x to y (and from y to x) for every pair of vertices (x, y), while having circuits or loops, that is, arcs that directly connect nodes with themselves, and multiple arrows with the same source and target nodes, thus covering all possible directed graphs as a Directed Acyclic Graphs (DAG) or weighted directed graphs/networks . The set of equations is a Structural Equation Model (SEM), showing the causal connections and the details of the relationship. SEMs represent all possible interrelationships between or among variables.