DataTalks.Club

Democratizing Causality - Aleksander Molak

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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
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