Stefan Wager—Professor at Stanford and expert on causal machine learning—has worked with leading tech companies including Dropbox, Facebook, Google, and Uber. He challenges the widespread assumption that better predictions mean better decisions. Traditional machine learning excels at prediction, but is prediction really what your business needs? Stefan explores why predictive models alone often fail to answer critical “what-if” questions, how causal machine learning bridges this gap, and provides practical advice for how you can start applying causal ML at work.
00:00 The Limitations of Prediction
01:08 Causal Machine Learning: A New Approach
04:20 Introducing Stefan Wager
04:23 The Importance of Causal Inference
07:31 Challenges and Adoption in Industry
15:52 Practical Examples and Case Studies
20:25 Implementing Causal ML in Organizations
25:14 The Value of Experiments in Causal Analysis
25:34 Challenges with Observational Data
26:12 Industry's Approach to Causal Inference
27:11 Historical Examples and Model Evaluation
28:30 Heuristics for Choosing Modeling Techniques
28:56 Tree-Based Methods and GRF Software
33:27 Communicating Causal ML Results
37:00 Explainable ML vs. Causal ML
40:37 Causal Discovery in Different Fields
42:44 Failure Modes in Causal ML
45:03 Industry vs. Academia in Causal ML
49:18 Resources for Learning Causal Inference
50:43 Future of Causal ML in Business
52:08 Final Thoughts and Common Sense in ML