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From correlation to causation: the fundamental challenge

Published on September 30, 2026   9 min

A selection of talks on Finance, Accounting & Economics

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0:00
Welcome back, everyone. My name is Alessio Volpicella, associate professor at the University of Pavia. That's going to be the second lecture of our journey to an introduction to econometrics. In particular, today, we're going to start looking into one of the key challenges in econometrics, the distinction between correlation and causation.
0:29
A short summary of what we saw in Lecture 1. We saw that basically econometrics turns economic questions into measurable and quantifiable answers by merging economic theories and models, data, and statistics.
0:48
We're going to build on that to introduce the trickiest part in econometrics. As I said, distinguishing causal effects from pure correlations. We're going to rely on some real-world simple examples to show why correlation doesn't imply causation, and we're going to start seeing how econometric techniques help uncover true causal relationships.
1:16
First of all, a bit of language, just to be sure we are all on the same page. By correlation, we mean that two variables, let's say X and Y move together, and you can think of any economic variable you may be interested in. That's different from causation, which implies that changing X makes Y change. Now, there are mostly three reasons why,

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From correlation to causation: the fundamental challenge

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