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Recorded on Aug 27, 2023 in München, Germany


Is Causality Necessary For Autonomous Driving?

From a child experimenter to a lead engineer working on a general causal inference engine, Daniel's choices have been marked by intense curiosity and the courage to take risks.

Daniel shares how working with mathematicians differs from working with physicists and how having both on the team makes the team stronger.

We discuss the journey Daniel and his team took to build a system that allows  performing the abduction step on a broad class of models in a computationally efficient way - a prerequisite to build a practically valuable counterfactual reasoning system.

Finally, Daniel shares his experiences in communicating with stakeholders and offers  advice for those of us who only begin their journey with causality.

Ready?

About The Guest
Daniel Ebenhöch is a Lead Engineer at e:fs Techhub. His research is focused on autonomous driving and automated decision-making. He leads a diverse team of scientists and developers, working on a general SCM-based causal inference engine.

Connect with Daniel:
- Daniel Ebenhöch on LinkedIn

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
Packages
- PGMpy (https://pgmpy.org/)

Books
- Molak (2023) - Causal Inference and Discovery in Python
- Pearl (2009) - Causality
- Peters et al. (2017) - Elements of Causal Inference: Foundations and Learning Algorithms

Causal Bandits Team
Project Coordinator: Taiba Malik
Video and Audio Editing: Na

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Causal Bandits Podcast
Causal AI || Causal Machine Learning || Causal Inference & Discovery
Web: https://causalbanditspodcast.com

Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/
Join Causal Python Weekly: https://causalpython.io
The Causal Book: https://amzn.to/3QhsRz4