High Signal: Data Science | Career | AI Episode 12: Your Machine Learning Solves The Wrong Problem
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Mar 13, 2025 Stefan Wager, a Stanford professor and expert in causal machine learning, dives into the misalignments between prediction and decision-making. He argues that traditional machine learning often neglects the crucial 'what-if' questions businesses face. Stefan shares insights on causal relationships and emphasizes the need for robust experimentation to make informed decisions. He explores the role of causal ML in enhancing customer engagement and optimizing revenue, while also discussing common pitfalls in experimental design, making a compelling case for collaborative learning in the data science field.
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Prediction vs. Causation
- Traditional machine learning excels at prediction, but prediction alone is insufficient for impactful decisions.
- Causal machine learning allows you to answer "what-if" questions and optimize for desired outcomes.
Airbnb Example
- In 2008, a prediction algorithm would have predicted no interest in Airbnb.
- This highlights how prediction is inadequate for driving progress in new or changing markets.
Causal ML Workflow
- Clarify your action space and desired outcomes before applying machine learning.
- Run experiments to gather data suitable for causal machine learning.
