talk-data.com

We talked about:

Aleksander's background Aleksander as a Causal Ambassador Using causality to make decisions Counterfactuals and and Judea Pearl Meta-learners vs classical ML models Average treatment effect Reducing causal bias, the super efficient estimator, and model uplifting Metrics for evaluating a causal model vs a traditional ML model Is the added complexity of a causal model worth implementing? Utilizing LLMs in causal models (text as outcome) Text as treatment and style extraction The viability of A/B tests in causal models Graphical structures and nonparametric identification Aleksander's resource recommendations

Links:

The Book of Why: https://amzn.to/3OZpvBk Causal Inference and Discovery in Python: https://amzn.to/46Pperr Book's GitHub repo: https://github.com/PacktPublishing/Causal-Inference-and-Discovery-in-Python The Battle of Giants: Causality vs NLP (PyData Berlin 2023): https://www.youtube.com/watch?v=Bd1XtGZhnmw New Frontiers in Causal NLP (papers repo): https://bit.ly/3N0TFTL

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

Unraveling the Secrets of causal Machine Learning - Aleksander Molak

About this event

Outline:

• What is causality in a machine learning sense?
• Causality and LLMs - how causal does ChatGPT think?
• Caveats and pitfalls of causal models
• Causal models in as a product in a business setting

About the guest:

About the speaker:

My name is Aleksander Molak. Friends call me Alex. My mission is to translate complex concepts into understandable bite-size pieces and share them with you. I am an independent machine learning researcher, author, consultant, educator and an entrepreneur. I am specialized in causality, natural language processing (NLP) and AI startegy.

DataTalks.Club is the place to talk about data. Join our slack community