What if We Identified Causes From Their Effects? — Assimilative Causal Inference

Assimilative causal inference is a new mathematical framework where causes are traced backwards from their observed effects, shifting the classical paradigm of predictive causality.

🖈Posted on: March 02, 2026 || 🖎 Last Update: March 02, 2026 || Category: Causal Inference, Stochastic Differential Equations, High-Dimensional Complex Systems, Data Assimilation, Information Theory, Nonlinear Geophysics