Ron Kohavi is a Technical Fellow and corporate VP of Microsoft's Analysis and Experimentation, and was previously director of data mining and personalization at Amazon. He received his Ph.D. in Computer Science from Stanford University. His papers have over 40,000 citations and three of them are in the top 1,000 most-cited papers in Computer Science.
Inhaltsangabe
Preface - how to read this book 1. Introduction and motivation 2. Running and analyzing experiments: an end-to-end example 3. Twyman's law and experimentation trustworthiness 4. Experimentation platform and culture Part II: 5. Speed matters: an end-to-end case study 6. Organizational metrics 7. Metrics for experimentation and the Overall Evaluation Criterion (OEC) 8. Institutional memory and aeta-analysis 9. Ethics in controlled experiments Part III: 10. Complementary techniques 11. Observational causal studies Part IV: 12. Client-side experiments 13. Instrumentation 14. Choosing a randomization unit 15. Ramping experiment exposure: trading off speed, quality, and risk 16. Scaling experiment analyses Part V: 17. The statistics behind online controlled experiments 18. Variance estimation and improved sensitivity: pitfalls and solutions 19. The A/A test 20. Triggering for improved sensitivity 21. Guardrail metrics 22. Leakage and interference between variants 23. Measuring long-term treatment effects.
Preface - how to read this book 1. Introduction and motivation 2. Running and analyzing experiments: an end-to-end example 3. Twyman's law and experimentation trustworthiness 4. Experimentation platform and culture Part II: 5. Speed matters: an end-to-end case study 6. Organizational metrics 7. Metrics for experimentation and the Overall Evaluation Criterion (OEC) 8. Institutional memory and aeta-analysis 9. Ethics in controlled experiments Part III: 10. Complementary techniques 11. Observational causal studies Part IV: 12. Client-side experiments 13. Instrumentation 14. Choosing a randomization unit 15. Ramping experiment exposure: trading off speed, quality, and risk 16. Scaling experiment analyses Part V: 17. The statistics behind online controlled experiments 18. Variance estimation and improved sensitivity: pitfalls and solutions 19. The A/A test 20. Triggering for improved sensitivity 21. Guardrail metrics 22. Leakage and interference between variants 23. Measuring long-term treatment effects.
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