When Factors Collide: Mapping Causal Spillovers across Global Asset Networks

Building on the theoretical work of Marcos López de Prado, Alexander Lipton, and Vincent Zoonekynd, who demonstrated that causal factor analysis is necessary for investment efficiency, this article develops a quantitative framework that makes causal factor investing operational. The proposed CAFNITE (<italic>Causal Asset and Factor Network Interference under Treatment Effects</italic>) model extends causal inference from model specification to network behavior, quantifying how shocks to one factor propagate across others through causal linkages. Using a dataset of 20 global factors and 20 assets from 2001 to 2024, CAFNITE estimates direct, indirect, and combined causal effects across equity, bond, currency, and commodity markets. The results reveal asymmetric, state-dependent transmission patterns: during calm regimes, spillovers are diffuse whereas under stress, a small set of factors, particularly the market, momentum, and the US dollar, dominates propagation. By linking causal modeling to factor interaction and diversification diagnostics, CAFNITE provides investors with a practical toolkit for monitoring causal dependencies, stress-testing portfolios, and designing dynamically resilient factor allocations. CAFNITE integrates causal factor inference and interference.