Ron Kohavi, Diane Tang, Ya Xu
Trustworthy Online Controlled Experiments
A Practical Guide to A/B Testing
Ron Kohavi, Diane Tang, Ya Xu
Trustworthy Online Controlled Experiments
A Practical Guide to A/B Testing
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This practical guide for students, researchers and practitioners offers real world guidance for data-driven decision making and innovation.
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This practical guide for students, researchers and practitioners offers real world guidance for data-driven decision making and innovation.
Produktdetails
- Produktdetails
- Verlag: Cambridge University Press
- Seitenzahl: 288
- Erscheinungstermin: 2. April 2020
- Englisch
- Abmessung: 228mm x 155mm x 17mm
- Gewicht: 404g
- ISBN-13: 9781108724265
- ISBN-10: 1108724264
- Artikelnr.: 58306747
- Verlag: Cambridge University Press
- Seitenzahl: 288
- Erscheinungstermin: 2. April 2020
- Englisch
- Abmessung: 228mm x 155mm x 17mm
- Gewicht: 404g
- ISBN-13: 9781108724265
- ISBN-10: 1108724264
- Artikelnr.: 58306747
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.
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.
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.
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.