What would the world look like if Russia had won the cold war? If the Boston Tea Party never happened? And where would we all be if Guido van Rossum had decided to pursue a career in theatre? Unfortunately we don't have the technology to slide into parallel worlds and explore alternative histories. However it turns out we *do* have the tools to simulate parallel realities and give decent answers to intriguing 'what if' questions. This talk will provide a gentle introduction to these tools, professionally known as Causal Inference.
The talk is aimed at data practitioners, preferably with basic knowledge of Python and statistics. That said, the focus of the talk is to nurture an intuitive understanding of the subject first, and implementation second. By the end of the talk I hope audience members could identify causal inference problems, have an intuitive understanding of the different tools they can apply to these problems, and have the appetite to further their learning!
The talk will cover the problem of answering causal questions (The Fundamental Problem of Causal Inference) and the main tools to address it. The emphasis will be on intuitive understanding how the different tools work, rather than pesky underlying assumptions, complex notation or convoluted literature. Just enough theory and lines of code to get the message across.
Outline:
- *Introduction to parallel universes and "what if?" questions?* [2 mins]
- *The golden standard for causal inference.* We'll discuss randomised controlled experiments and also set the scene for cases these aren't possible. [6 mins]
Three key tools:
- *Differences-in-Differences* [3 mins]
- *Propensity score methods* [4 mins]
- *Synthetic Controls* (or: creating an alternate universe on your machine) [5 mins]
What's next: [5 mins]
A wrap up that includes:
- *What we didn't cover* (a few words about other techniques, DAGs, etc.)
- *Quick overview of Python tools for causal inference*
- *Where do we go from here* - resources, curriculums, readings and communities.
