tangzhenyu / experimentation-resources

A collection of experimentation papers/books/articles that I found useful :smile:

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Experimentation resources 📝



Books

Title Link
Kohavi, R., Tang, D. and Xu, Y., 2020. Trustworthy online controlled experiments: A practical guide to a/b testing. Cambridge University Press. [link]
Fabijan, Alexander PhD Thesis “Data-Driven Software Development at Large Scale: from Ad-Hoc Data Collection to Trustworthy Experimentation”. [link]
Georgiev, G., 2019, Statistical Methods in Online A/B Testing: Statistics for data-driven business decisions and risk management in e-commerce [link]

Articles

Title Link
Fabijan, A., ExP, E.P., Arai, B., Dmitriev, P. and Vermeer, L., It takes a Flywheel to Fly: Kickstarting and Growing the A/B testing Momentum at Scale. [link]
Gupta, S., Kohavi, R., Tang, D., Xu, Y., Andersen, R., Bakshy, E., Cardin, N., Chandran, S., Chen, N., Coey, D. and Curtis, M., 2019. Top challenges from the first practical online controlled experiments summit. ACM SIGKDD Explorations Newsletter, 21(1), pp.20-35. [link]
Fabijan, A., Dmitriev, P., Olsson, H.H., Bosch, J., Vermeer, L. and Lewis, D., 2019, May. Three key checklists and remedies for trustworthy analysis of online controlled experiments at scale. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) (pp. 1-10). IEEE. [link]
Fabijan, A., Gupchup, J., Gupta, S., Omhover, J., Qin, W., Vermeer, L. and Dmitriev, P., 2019, July. Diagnosing sample ratio mismatch in online controlled experiments: a taxonomy and rules of thumb for practitioners. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2156-2164) [link]
Shi, X., Dmitriev, P., Gupta, S. and Fu, X., 2019, July. Challenges, best practices and pitfalls in evaluating results of online controlled experiments. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3189-3190). [link]
Fabijan, A., Dmitriev, P., McFarland, C., Vermeer, L., Holmström Olsson, H. and Bosch, J., 2018. Experimentation growth: Evolving trustworthy A/B testing capabilities in online software companies. Journal of Software: Evolution and Process, 30(12), p.e2113. [link]
Deng, A., Li, Y., Lu, J. and Ramamurthy, V., 2021. On Post-selection Inference in A/B Testing. [link]
Machmouchi, W., Awadallah, A.H., Zitouni, I. and Buscher, G., 2017, November. Beyond success rate: Utility as a search quality metric for online experiments. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (pp. 757-765). [link]
Hohnhold, H., O'Brien, D. and Tang, D., 2015, August. Focusing on the Long-term: It's Good for Users and Business. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1849-1858). [link]
Tang, D., Agarwal, A., O'Brien, D. and Meyer, M., 2010, July. Overlapping experiment infrastructure: More, better, faster experimentation. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 17-26). [link]
Duan, W., Ba, S. and Zhang, C., 2021, March. Online Experimentation with Surrogate Metrics: Guidelines and a Case Study. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (pp. 193-201). [link]
Xu, Ya, et al. "From infrastructure to culture: A/b testing challenges in large scale social networks." Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2015. [pdf]
Chen, N., Liu, M. and Xu, Y., 2019, January. How A/B tests could go wrong: Automatic diagnosis of invalid online experiments. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (pp. 501-509). [link]
Xu, Y., Duan, W. and Huang, S., 2018, July. SQR: balancing speed, quality and risk in online experiments. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 895-904). [link]
Chen, N., Liu, M. and Xu, Y., 2018. Automatic Detection and Diagnosis of Biased Online Experiments. arXiv preprint arXiv:1808.00114 [link]
Sadeghi, S., Gupta, S., Gramatovici, S., Lu, J., Ai, H. and Zhang, R., 2021. Novelty and Primacy: A Long-Term Estimator for Online Experiments. arXiv preprint arXiv:2102.12893. [link]
Gupta, Somit, et al. "The anatomy of a large-scale experimentation platform." 2018 IEEE International Conference on Software Architecture (ICSA). IEEE, 2018. [link]
Fabijan, Aleksander, et al. "Online controlled experimentation at scale: an empirical survey on the current state of A/B testing." 2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, 2018. [link]
Fabijan, Aleksander, et al. "Effective online controlled experiment analysis at large scale." 2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, 2018. [link]
Fabijan, Aleksander, et al. "The evolution of continuous experimentation in software product development: from data to a data-driven organization at scale." 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE). IEEE, 2017. [link]
Deng, A., Xu, Y., Kohavi, R. and Walker, T., 2013, February. Improving the sensitivity of online controlled experiments by utilizing pre-experiment data. In Proceedings of the sixth ACM international conference on Web search and data mining (pp. 123-132). [link]
Deng, A. and Shi, X., 2016, August. Data-driven metric development for online controlled experiments: Seven lessons learned. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 77-86). [link]
Kohavi, R., Deng, A., Longbotham, R. and Xu, Y., 2014, August. Seven rules of thumb for web site experimenters. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1857-1866). [link]
Deng, A., Li, T. and Guo, Y., 2014, April. Statistical inference in two-stage online controlled experiments with treatment selection and validation. In Proceedings of the 23rd international conference on World Wide Web (pp. 609-618). [link]
Kohavi, R., Deng, A., Frasca, B., Longbotham, R., Walker, T. and Xu, Y., 2012, August. Trustworthy online controlled experiments: Five puzzling outcomes explained. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 786-794). [link]
Gupchup, J., Hosseinkashi, Y., Dmitriev, P., Schneider, D., Cutler, R., Jefremov, A. and Ellis, M., 2018, October. Trustworthy experimentation under telemetry loss. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (pp. 387-396). [link]
Kohavi, R., Crook, T., Longbotham, R., Frasca, B., Henne, R., Ferres, J.L. and Melamed, T., 2009. Online experimentation at Microsoft. Data Mining Case Studies, 11(2009), p.39. [link]
Dmitriev, P., Gupta, S., Kim, D.W. and Vaz, G., 2017, August. A dirty dozen: twelve common metric interpretation pitfalls in online controlled experiments. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1427-1436). [link]
Xia, T., Bhardwaj, S., Dmitriev, P. and Fabijan, A., 2019, May. Safe velocity: a practical guide to software deployment at scale using controlled rollout. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) (pp. 11-20). IEEE. [link]
Wang, Y., Gupta, S., Lu, J., Mahmoudzadeh, A. and Liu, S., 2019, November. On heavy-user bias in a/b testing. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 2425-2428). [link]
Xia, T., Bhardwaj, S., Dmitriev, P. and Fabijan, A., 2019, May. Safe velocity: a practical guide to software deployment at scale using controlled rollout. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) (pp. 11-20). IEEE. [link]
Machmouchi, W. and Buscher, G., 2016, July. Principles for the design of online A/B metrics. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval (pp. 589-590). [link]
Lu, J., Qiu, Y. and Deng, A., 2019. A note on Type S/M errors in hypothesis testing. British Journal of Mathematical and Statistical Psychology, 72(1), pp.1-17. [link]
Deng, A., Knoblich, U. and Lu, J., 2018, July. Applying the Delta method in metric analytics: A practical guide with novel ideas. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 233-242). [link]
Mattos, D.I., Dmitriev, P., Fabijan, A., Bosch, J. and Olsson, H.H., 2018, November. An activity and metric model for online controlled experiments. In International Conference on Product-Focused Software Process Improvement (pp. 182-198). Springer, Cham. [link]
Fabijan, A., Dmitriev, P., Olsson, H.H. and Bosch, J., 2017, August. The benefits of controlled experimentation at scale. In 2017 43rd Euromicro Conference on Software Engineering and Advanced Applications (SEAA) (pp. 18-26). IEEE. [link]
Diamantopoulos, Nikos, et al. "Engineering for a science-centric experimentation platform." Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering in Practice. 2020. [link]
Forsell, Eskil, et al. "Success Stories from a Democratized Experimentation Platform." arXiv preprint arXiv:2012.10403 (2020). [link]
Xie, H. and Aurisset, J., 2016, August. Improving the sensitivity of online controlled experiments: Case studies at netflix. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 645-654). [link]
Lopez Kaufman, Raphael, Jegar Pitchforth, and Lukas Vermeer. "Democratizing online controlled experiments at Booking. com." arXiv e-prints (2017): arXiv-1710. [link] [video]
Kluck, T. and Vermeer, L., 2017. Leaky Abstraction In Online Experimentation Platforms: A Conceptual Framework To Categorize Common Challenges. arXiv preprint arXiv:1710.00397. [link] [post]
Öztan, B.T., van Havre, Z., Gomes, C. and Vermeer, L., 2018. Mediation Analysis in Online Experiments at Booking. com: Disentangling Direct and Indirect Effects. arXiv preprint arXiv:1810.12718. [link]
Karrer, Brian, et al. "Network experimentation at scale." arXiv preprint arXiv:2012.08591 (2020). [link]
Tosch, Emma, et al. "PlanAlyzer: assessing threats to the validity of online experiments." Proceedings of the ACM on Programming Languages 3.OOPSLA (2019): 1-30. [link]
Bakshy, E. and Frachtenberg, E., 2015, May. Design and analysis of benchmarking experiments for distributed internet services. In Proceedings of the 24th International Conference on World Wide Web (pp. 108-118). [link]
Bakshy, E., Eckles, D. and Bernstein, M.S., 2014, April. Designing and deploying online field experiments. In Proceedings of the 23rd international conference on World wide web (pp. 283-292). [link]
Zhao, Z., Chen, M., Matheson, D. and Stone, M., 2016, October. Online experimentation diagnosis and troubleshooting beyond aa validation. In 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (pp. 498-507). IEEE. [link]
Drutsa, A., Gusev, G. and Serdyukov, P., 2015, May. Future user engagement prediction and its application to improve the sensitivity of online experiments. In Proceedings of the 24th International Conference on World Wide Web (pp. 256-266). [link]
Kharitonov, E., Vorobev, A., Macdonald, C., Serdyukov, P. and Ounis, I., 2015, August. Sequential testing for early stopping of online experiments. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 473-482). [link]
Kharitonov, E., Macdonald, C., Serdyukov, P. and Ounis, I., 2015, August. Optimised scheduling of online experiments. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 453-462). [link]
Budylin, R., Drutsa, A., Katsev, I. and Tsoy, V., 2018, February. Consistent transformation of ratio metrics for efficient online controlled experiments. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (pp. 55-63). [link]
Budylin, R., Drutsa, A., Gusev, G., Serdyukov, P. and Yashkov, I., 2018. Online evaluation for effective web service development. arXiv preprint arXiv:1809.00661. [link]
Lindon, M. and Malek, A., 2020. Sequential Testing of Multinomial Hypotheses with Applications to Detecting Implementation Errors and Missing Data in Randomized Experiments. arXiv preprint arXiv:2011.03567. [link] [post]
Johari, R., Koomen, P., Pekelis, L. and Walsh, D., 2017, August. Peeking at a/b tests: Why it matters, and what to do about it. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1517-1525). [link]
Ha-Thuc, V., Dutta, A., Mao, R., Wood, M. and Liu, Y., 2020, July. A counterfactual framework for seller-side a/b testing on marketplaces. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2288-2296). [link]
Liu, M., Mao, J. and Kang, K., 2021, August. Trustworthy and Powerful Online Marketplace Experimentation with Budget-split Design. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 3319-3329). [link]
Nandy, P., Venugopalan, D., Lo, C. and Chatterjee, S., 2021. A/B Testing for Recommender Systems in a Two-sided Marketplace. arXiv preprint arXiv:2106.00762. [link]
Gelman, A. and Carlin, J., 2014. Beyond power calculations: Assessing type S (sign) and type M (magnitude) errors. In Perspectives on Psychological Science, 9(6), pp.641-651. [link]

Blog posts & other

Title Link
Thomke, Stefan. "Building a culture of experimentation." Harvard Business Review 98.2 (2020): 40-47. [link]
Experiment reporting framework, Airbnb [link]
Why we use experimentation quality as the main KPI for our experimentation platform, Booking [link]
How We Reimagined AB Testing at Squarespace [link]
How Etsy Handles Peeking in A/B tests [link]
Interview with Experimentation Lead: Andre Richter at Just Eat Takeaway.com [link]
Our evolution towards T-REX: The prehistory of experimentation infrastructure at LinkedIn [link]
Making the LinkedIn experimentation engine 20x faster, LinkedIn [link]
Designing an Experimentation Platform, Zalando [podcast]
AB testing at Zalando: concepts and tools [video]
Experimentation Platform at Zalando: Part 1 - Evolution [link]
A Conversation with Ronny Kohavi (ex-Airbnb, Microsoft, and Amazon) [podcast]
How to Use Quasi-experiments and Counterfactuals to Build Great Products, Shopify [link]
Quasi Experimentation at Netflix [link]
Decision Making at Netflix Series: [1 2 3 4 5]

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A collection of experimentation papers/books/articles that I found useful :smile:

License:MIT License