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Causal AI, Effect Heterogeneity & Understanding ML || Alicia Curth || Causal Bandits Ep. 006 (2023)
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Video version available on YouTube
Recorded on Nov 29, 2023 in Cambridge, UK
Should we continue to ask why?
Alicia's machine learning journey began with... causal machine learning.
Starting with econometrics, she discovered semi-parametric methods and the Pearlian framework at later stages of her career and incorporated both in her everyday toolkit.
She loves to understand why things work, which inspires her to ask "why" not only in the context of treatment effects, but also in the context of general machine learning. Her papers on heterogeneous treatment effect estimators and model evaluation bring unique perspectives to the community.
Her recent NeurIPS paper on double descent aims at bridging the gap between statistical learning theory and a counter-intuitive phenomenon of double descent observed in complex machine learning architectures.
Ready to dive in? ------------------------------------------------------------------------------------------------------ About The Guest
Alicia Curth is a Machine Learning Researcher and a final year PhD student at The van der Schaar Lab at Cambridge University. Her research is focused on causality, understanding machine learning methods from ground up and personalized medicine. Her works are frequently accepted at best machine learning conferences (she's a true serial NeurIPS author).
Connect with Alicia:
- Alicia on Twitter/X
- Alicia on LinkedIn
- Alicia '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
See here for the full list of links
Causal Bandits Team
Project Coordinator: Taiba Mal
Support the show
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
View all episodes
By Alex Molak
5
77 ratings
Send us a text
Support the show
Video version available on YouTube
Recorded on Nov 29, 2023 in Cambridge, UK
Should we continue to ask why?
Alicia's machine learning journey began with... causal machine learning.
Starting with econometrics, she discovered semi-parametric methods and the Pearlian framework at later stages of her career and incorporated both in her everyday toolkit.
She loves to understand why things work, which inspires her to ask "why" not only in the context of treatment effects, but also in the context of general machine learning. Her papers on heterogeneous treatment effect estimators and model evaluation bring unique perspectives to the community.
Her recent NeurIPS paper on double descent aims at bridging the gap between statistical learning theory and a counter-intuitive phenomenon of double descent observed in complex machine learning architectures.
Ready to dive in? ------------------------------------------------------------------------------------------------------ About The Guest
Alicia Curth is a Machine Learning Researcher and a final year PhD student at The van der Schaar Lab at Cambridge University. Her research is focused on causality, understanding machine learning methods from ground up and personalized medicine. Her works are frequently accepted at best machine learning conferences (she's a true serial NeurIPS author).
Connect with Alicia:
- Alicia on Twitter/X
- Alicia on LinkedIn
- Alicia '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
See here for the full list of links
Causal Bandits Team
Project Coordinator: Taiba Mal
Support the show
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
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