This post was originally written and published in French, Corrélation et causalité, circonstance et contexte
Let’s imagine we collected a few pieces of information, for a clearly identified individuals, via connected devices,
- Thursday 18:30: a €45 purchase at a bar-tabac
- Friday 13:15, 1 hour above Porte Saint-Martin
- Saturday 14:00, 1 hour near Place de la République

Trying to make the data “speak”, we might hesitate between a first version
- Thursday 18:30: a €45 purchase of cigarettes
- Friday 13:15, 1 hour at the mosque, at prayer time
- Saturday 14:00, 1 hour in a demonstration that started from Place de la République in the early afternoon
and a second version
- Thursday 18:30: a €45 purchase of tax stamps
- Friday 13:15, 1 hour at the gym
- Saturday 14:00, 1 hour at the hairdresser’s, Place de la République
In other words: with the very same raw signal, we can build two perfectly coherent stories. This is not an artistic trick à la Sophie Calle; it is the standard situation as soon as we work with traces (geolocation, timestamps, spending, calls, driving). Data do not necessarily lie. But they underdetermine the narrative, because they allow several possible worlds. In insurance (and more broadly in the economy of prediction), we are used to taking correlations and turning them into decisions—not only to “understand”, but to price, classify, accept, refuse. The point is not to say this is illegitimate in principle. The point is to recall what we do when we only correlate, and what we forget when we have no context.
Continue reading Correlation, Causation, Circumstance, Context



