Analytics Data ScienceSee in schedule: Thu, Jul 29, 14:15-15:00 CEST (45 min) Download/View Slides
Correlation does not imply causation. It turns out, however, that with some simple ingenious tricks one can unveil causal relationships within standard observational data, without having to resort to expensive random control trials.
I introduce the basic concepts of causal inference demonstrating in an accessible manner using visualisations. My main message for data analysts is that by adding causal inference to your statistical toolbox you are likely to conduct better experiments and ultimately get more from your data.
E.g, by introducing Simpson’s Paradox, a situation where the outcome of all entries is in conflict with that of its cohorts, I shine a light on the importance of using graphs to model the data which enables identification and managing confounding factors.
This talk is targeted towards anyone making data driven decisions. The main takeaway is the importance of the story behind the data is as important as the data itself.
My ultimate objective is to whet your appetite to explore more on the topic, as I believe that it will enable you to go beyond correlation calculations and extract more insights from your data, as well as avoid common misinterpretation pitfalls like Simpson’s Paradox.
Type: Talk (45 mins); Python level: Beginner; Domain level: Beginner
Ex-cosmologist turned data scientist with over 15 years experience in solving challenging problems. I am motivated by intellectual challenges, highly detail oriented and love visualising data results to communicate insights for better decisions within organisations.
My main drive as a data scientist is applying scientific approaches that result in practical and clear solutions. To accomplish these, I use whatever works, be it statistical/causal inference, machine/deep learning or optimisation algorithms. Being result driven I have a passion for quantifying and communicating the impact of interventions to non-specialist audiences in an accessible manner.
My claim for fame is between 2004-2014 living in four different continents within a span of a decade, including three tennis Grand Slam cities (NYC, Melbourne, London).