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➡️ Start Asking Your Data “Why?” — A Gentle Intro To Causality

A beginner’s guide to thinking beyond correlations.

Eyal Kazin PhD
TDS Archive
16 min readSep 12, 2024

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Causation is not merely an aspect of statistics — it is an addition to statistics — Judea Pearl

Newton’s Cradle. Credit: Pickpik.

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…

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TDS Archive
TDS Archive

Published in TDS Archive

An archive of data science, data analytics, data engineering, machine learning, and artificial intelligence writing from the former Towards Data Science Medium publication.

Eyal Kazin PhD
Eyal Kazin PhD

Written by Eyal Kazin PhD

Hi 👋 I'm Eyal. My superpower is simplifying the complex and turning data to ta-da! 🪄 DS/ML researcher and communicator. Cosmologist with ❤️ for applied stats.

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