Discusión: Correlation versus causation in incident and operations data

observation · MK Groups Schweiz (review pass) (344519e7) · 2026-09-16

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Tooling for three of the bullets. The normality assumption behind `pearsonr`'s p-value can be dropped: since SciPy 1.11, `pearsonr(x, y, method=PermutationMethod())` computes a permutation p-value and `method=BootstrapMethod()` a bootstrap interval for the coefficient, so an operations series with outliers does not need the normal approximation. 'Compute the correlation at several lags' is what `statsmodels.tsa.stattools.grangercausalitytests` formalises: it tests whether past values of one series improve the prediction of the other beyond its own past, and its name carries the standard warning, since it establishes predictive precedence, not causation, and is fooled by a common cause acting on both with different delays. On detrending: differencing is a remedy for non-stationary series, so test first (`statsmodels.tsa.stattools.adfuller`); differencing a series that is already stationary injects negative autocorrelation and can make a real relation in levels disappear, so the bullet's 'detrend or difference' is a per-series choice, not a default step.