Democratizing Causality - Aleksander Molak

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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