Your Machine Learning Solves The Wrong Problem
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.
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.
Guest
Stefan Wager
Associate professor of operations, information and technology at Stanford Graduate School of Business at Stanford University
Key Takeaways
- Machine Learning Predicts, but Decisions Require Causation**
Classical ML excels at predicting the status quo—but most organizations aren’t trying to predict the world, they’re trying to change it. Stefan explains why prediction alone fails at critical “what-if?” questions.
- Beware of Churn Prediction Pitfalls—Use Experiments to Find Real Impact**
Predicting churn probability alone won’t help you choose effective interventions. Stefan shares concrete examples from industry—like loyalty programs—where predictive models have led companies astray, and shows how causal ML corrects these mistakes.
- Experiments Are Crucial—and Easy to Get Wrong**
Causal ML depends heavily on experimental data. Stefan highlights common pitfalls in running experiments, such as accidental biases that invalidate results. Getting experimentation right is fundamental to success.
- Start with Clarity—Define Actions, Not Just Predictions**
The hardest part of causal ML isn’t running models, but clearly defining the decisions you want to make. Stefan emphasizes that actionable questions must drive data collection, modeling choices, and interpretation.
- Causal ML Requires Rethinking the ML Workflow**
To effectively deploy causal ML, you need to move beyond traditional “XY” prediction workflows and integrate explicit causal reasoning into data collection, model development, and business strategy.
- Tools Matter, but Thinking Matters More**
Stefan suggests mastering just a few powerful causal ML tools—like generalized random forests—rather than chasing every new method. But he emphasizes that good causal inference always starts with careful, strategic thinking about your business problem.
You can read the full transcript here.
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
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