Causal ML, Transparency & Time-Varying Treatments || Iyar Lin || Causal Bandits Ep. 008 (2024)

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Recorded on Sep 13, 2023 in Beit El'Azari, Israel


The eternal dance between the data and the model

Early in his career, Iyar realized that purely associative models cannot provide him with the answers to the questions he found most interesting.

This realization laid the groundwork for his search for methods that go beyond statistical summaries of the data.

What started as a lonely journey, led him to become a data science lead at his current company, where he fosters causal culture daily.

Iyar developed a framework that helps digital product companies make better decisions regarding their products at scale and at budget.

Here, causality is not just a concept, but a tool for change.

Ready to dive in?

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About The Guest
Iyar Lin is a Data Science Lead at Loops, where he helps customers make better decisions leveraging causal inference and machine learning methods. He holds master's degree in statistics from The Hebrew University of Jerusalem. Before Loops, he worked at ViaSat and SimilarWeb.

Connect with Iyar:
- Iyar on LinkedIn
- Iyar'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 (https://amzn.to/3QhsRz4).

Connect with Alex:
- Alex on the Internet

Links
Papers
- Breiman (2001) - Statistical Modeling: The Two Cultures

Books
- Molak (2023) - Causal Inference and Discovery in Python
- Pearl et al. (2016) - Causal Inference in Statistics - A Pri

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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