Correlation does not imply causation. It turns out, however, that with some simple ingenious tricks one can unveil causal relationships with readily available data, without having to resort to expensive randomised control trials. Learn how to make the most out of your data, avoid misinterpretation pitfalls and draw more meaningful conclusions by adding causal inference to your toolbox.
Are you interested in understanding Causal Inference but not sure where to start? In this talk I introduce the basic concepts demonstrated in an accessible manner using visualisations as well as python scripts.
In particular I illustrate the utility of Graph Models to visualise the story behind the data which enables going beyond correlations to make data driven decisions based on causation.
You will also learn how to avoid data misinterpretation pitfalls such as Simpson’s Paradox, a situation where the outcome of a population is in conflict with that of its cohorts. This will be demonstrated using pgmpy as well as a streamlit interactive web app: bit.ly/simpson-calculator.
This talk is targeted to anyone, technical or managerial, that wants to improve how they make data driven decisions. No prior knowledge in python is required; basic statistics is desirable but not essential. My main message is that by adding causal thinking to your analytical toolbox you are likely to ask better questions from data and ultimately get more insights from it.
For those inclined to learn more in depth about Causal Inference, I will summarise with advice on how to climb the "causal ladder" by suggesting resources like books and programming packages that I find useful.
Slide Deck: bit.ly/start-ask-why
Blog Posts: bit.ly/start-ask-why-post
Annotated Google Colab Jupyter notebook to learn Simpson's Paradox: bit.ly/pgmpy-simpsons-demo
Streamlit interactive demo of Simpson's Paradox: bit.ly/simpson-calculator