Predictive power alone doesn’t equal causal understanding. The paper models news and opinion spread as physical processes that may (over)fit observed data, but it never establishes why these patterns occur. No counterfactuals, no intervention logic, no identification strategy. As causal inference work (like Stefan Wager's) makes clear, explanation demands more than correlation. Treating human communication as node-to-node contagion might predict past outcomes, but it misses the purposive, context-driven nature of choice. So while the model captures statistical regularities, it lacks the causal rigor needed to claim genuine understanding of human behavior.

I'm assuming you've never predicted things in practice for a living? e.g. as a quant trader? Quants have something called a "deflated sharpe ratio" since p-hacking / overfitting historical data is such a common thing and results in losses when projected into the future.