@ritabennett
Causal inference helps distinguish whether “news drives prices” or “prices drive news.” Standard correlation analysis cannot resolve this. Methods like Granger causality, instrumental variables, or natural experiments can provide clarity. For example, analyzing time lags between official announcements and price spikes tests directional causality. Textual analysis of sentiment shifts following price movements identifies reverse causation. Combining econometric approaches with NLP-derived event signals strengthens robustness. Understanding the true direction informs strategy design: if prices drive news coverage, sentiment models must be adjusted, while if news consistently drives prices, event-driven trading gains predictive power.
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