Analytics Data ScienceSee in schedule
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.
The talk is targeted towards intermediate and expert analysts as well as researchers. Participants should have a basic understanding of statistical concepts.
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 pitfalls like Simpson’s Paradox.
Type: Talk (45 mins); Python level: Intermediate; Domain level: Intermediate
Ex-cosmologist turned data scientist with over 10 years experience in machine/deep learning, statistical inference, and passionate about data insights visualisations. Result driven and highly detail oriented I’m motivated by intellectual challenges and love analysing data to communicate insights for better decisions within organisations.
I currently work in Healthcare and am avidly learning about Causal Inference, which I find a fascinating method to derive more value from data.
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 (NY, Melbourne, London).