1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

5. 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.

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You can read the full transcript here.

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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

• Stefan's Stanford Website
• Machine Learning and Economics, Stefan and Susan Athey's lectures for the Stanford Graduate School of Business
• Causal Inference: A Statistical Learning Approach (WIP!)
• Mastering ‘Metrics: The Path from Cause to Effect by Angrist & Pischke
• The Book of Why: The New Science of Cause and Effect by Judea Pearl and Dana Mackenzie
• Causal Inference: The Mixtape by Scott Cunningham
• A Technical Primer On Causality by Adam Kelleher
• What Is Causal Inference? An Introduction for Data Scientists by Hugo Bowne-Anderson and Mike Loukides
• The Episode on YouTube
• Delphina's Newsletter

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