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Was Deep Learning Revolution Bad For Causal Inference?

Did deep learning revolution slowed down the progress in causal research?

Can causality help in finding drug repurposing candidates?

What are the main challenges in using causal inference at scale?

Ehud Karavani, the author of the CausalLib Python library and Researcher at IBM Research shares his experiences and thoughts on these challenging questions.

Ehud believes in the power of good code, but for him code is not only about software development.

He sees coding as an inseparable part of modern-day research.

A powerful conversation for anyone interested in applied causal modeling.

In this episode we discuss:

• Can causality help in finding drug repurposing candidates?
• Challenges in data processing for causal inference at scale
• Motivation behind Python causal inference library CausalLib
• Working at IBM Research Ready to dive in? 

About The Guest
Ehud Karavani, MSc is Research Staff Member at IBM Research in the Causal Machine Learning for Healthcare & Life Sciences Group. He focuses on high-throughput causal inference for finding new indications for existing drugs using electronic health records and insurance claims data. He's the original author of Causallib - one of the first Python libraries specialized in causal inference.

Connect with Ehud:

• Ehud on Twitter/X
• Ehud on LinkedIn
• Ehud's web page 

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 on the Internet

Links
Links for this episode can be found here

Video version of this episode can be found here. 

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