Causal Data Science
I started a series of posts aimed at helping people learn about causality in data science (and science in general), and wanted to compile them all together here in a living index. This list will grow as I post more:
The goal of this post is to develop a basic understand of the intuition behind causal graphs. It’s aimed at a general audience, and by the end of it, you should be able to intuitively understand causal diagrams, and reason about ways that the picture might be incomplete.
This post aims at a general audience. The goal is to understand what bias is, where it comes from, and how drawing a causal diagram can help you reason about bias.
The goal of this article is to understand some common errors in data analysis, and to motivate a balance of data resources to fast (correlative) and slow (causal) insights.
This is a very technical introduction to the material from the previous posts, aimed at practitioners with a background in regression analysis and probability.
In order to understand observational, graphical causal inference, you need to understand “conditional independence testing”. CIT can be sensitive to how you encode your data, and it’s a problem that is sometimes swept under the rug. This article brings it into the spotlight, and is a pre-cursor to our discussion on causal inference!
If you can’t experiment on a system, is there any hope for establishing causality? In some cases, with certain assumptions (and not the usual “no latent variables” ones!!), the answer is “yes”. In this post, I present a teaser on some relatively old work that has been done on the subject. Next time, we’ll dig deeply into how this works!
7. An observational criterion for causation (coming soon)…