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➡️ Start Asking Your Data “Why?” — A Gentle Intro To Causality
A beginner’s guide to thinking beyond correlations.
Causation is not merely an aspect of statistics — it is an addition to statistics — Judea Pearl
Correlation does not imply causation. It turns out, however, that with some simple ingenious tricks one can, potentially, unveil causal relationships within standard observational data, without having to resort to expensive randomised control trials.
This post is targeted towards anyone making data driven decisions. The main takeaway message is that causality may be possible by understanding that the story behind the data is as important as the data itself.
By introducing Simpson’s and Berkson’s Paradoxes, situations where the outcome of a population is in conflict with that of its cohorts, I shine a light on the importance of using causal reasoning to identify these paradoxes in data and avoid misinterpretation. Specifically I introduce causal graphs as a method to visualise the story behind the data point out that by adding this to your arsenal you are likely to conduct better analyses and experiments.
My ultimate objective is to whet your appetite to explore more on causality, as I believe…