Causal AI & Individual Treatment Effects | Scott Mueller Ep. 20 | CausalBanditsPodcast.com

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Can we say something about YOUR personal treatment effect?
The estimation of individual treatment effects is the Holy Grail of personalized medicine.
It's also extremely difficult.
Yet, Scott is not discouraged from studying this topic.
In fact, he quit a pretty successful business to study it.
In a series of papers, Scott describes how combining experimental and observational data can help us understand individual causal effects.
Although this sounds enigmatic to many, the intuition behind this mechanism is simpler than you might think.
In the episode we discuss:
🔹 What made Scott quit a successful business he founded and study causal inference?
🔹 How a false conviction about his own skills helped him learn? 🔹 What are individual treatment effects?
🔹 Can we really say something about individual treatment effects?
Ready to dive in?
About The Guest
Scott Mueller is a researcher and a PhD candidate in causal modeling at UCLA, supervised by Prof. Judea Pearl. He's a serial entrepreneur and the founder of UCode, a coding school for kids. His current research focuses on the estimation of individual treatment effects and their bounds. He works under the supervision of professor Judea Pearl.
Connect with Scott:
- Scott on Twitter/X
- Scott's webpage
About The Host
Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality.
Connect with Alex:
- Alex

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Bölümler

1. Causal AI & Individual Treatment Effects | Scott Mueller Ep. 20 | CausalBanditsPodcast.com (00:00:00)

2. [Ad] The Multiverse Employee Handbook (00:24:20)

3. (Cont.) Causal AI & Individual Treatment Effects | Scott Mueller Ep. 20 | CausalBanditsPodcast.com (00:24:58)