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
