Introduction to Causal Inference

Eyal Kazin, Staff Data Scientist at Babylon, encourages you to ask your data “Why?” in this introduction to causal inference.

Correlation does not imply causation. However with some simple tricks one can unveil causal relationships within standard observational data, without having to resort to expensive randomised control trials. In this talk Eyal introduces the basic concepts of causal inference and suggests that by adding causal inference to your statistical toolbox you are likely to conduct better experiments and ultimately get more from your data.
This session also introduces Simpson’s Paradox, a situation where the outcome of a population is in conflict with that of its cohorts, which shines a light on the importance of using graphs to model the data to enable identification and help manage confounding factors.

This talk will be of interest to anyone making data driven decisions and highlights importance of the story behind the data is as important as the data itself. By asking your data “Why?” you will be able to go beyond correlation calculations and extract more insights from your data, as well as avoid common misinterpretation pitfalls like Simpson’s Paradox.

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