We just published a tutorial on getting started with Causal AI in Python using the Do Why library at DataCamp. Thanks to my coauthor Jermain Kaminski and Matt Crabtree for the smooth editorial process. In the coming weeks there will also be a DataFramed podcast episode released in which I get deeper into the topic of causality and why it’s crucial for effective business decision-making. So keep an eye on your favorite podcast app!
Category: Causal AI
Practical AI Podcast – Causal Inference
With the folks over at the Practical AI podcast I talked about how to tackle robustness, fairness, and explainability with Causal AI. Check out the episode here:
Practical AI 220: Causal inference – Listen on Changelog.com
//cdn.changelog.com/embed.jsMapping Unchartered Territory
A frequent point of criticism against Directed Acyclic Graphs is that writing them down for a real-world problem can be a difficult task. There are numerous possible variables to consider and it’s not clear how we can determine all the causal relationships between them. We recently had a Twitter discussion where exactly this argument popped up again.
Paul Hünermund, Ph.D. 