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



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