A Python package for the statistical analysis of A/B tests
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Updated
Mar 7, 2026 - Python
A Python package for the statistical analysis of A/B tests
因果推理&AB实验相关论文小书库
Theory and implementatuon of various A/B experiments
AB testing, stattests, poisson bootstrap, cuped, linearization
Independent validation suite for Zetyra statistical calculators. 169 tests across GSD, CUPED, and Bayesian Toolkit — benchmarked against gsDesign, scipy, conjugate priors, and published trials (HPTN 083, HeartMate II, REBYOTA).
A/B testing statistics, design, increasing sensitivity, multiple experiments comparison, traffic splitting and full A/B testing pipeline in Python
Тыкаюсь в темах интенсива A/B-week от ШАДа
An experimental Python library for advanced A/B testing analysis, leveraging statistical techniques and ML for deeper insights.
End-to-end A/B experimentation toolkit: experiment readouts, MDE & power calculations, CUPED variance reduction, and decision-focused reporting in a Big Tech economist style.
Experimentation and causal inference platform for product decision systems, implementing A/B testing, CUPED variance reduction, and Difference-in-Differences analysis with reproducible pipelines.
A/B test lift simulation & analysis in Python: CUPED variance reduction, power analysis, and guardrail metrics with reproducible pytest CI.
Modern A/B experimentation utilities: CUPED/CUPAC hooks, triggered analysis, SRM, switchback helpers, and power sims.
Production A/B testing framework with Budget A/B, CUPED variance reduction, and sequential testing - DoorDash methodology
Educational projects on online experiments
Beauty supply chain demand sensing & inventory optimization — 6-model routing ensemble (MAPE 11.8%), X-Learner uplift (AUUC 0.74), CUPED A/B testing (-55%), shelf-life FIFO, 5-page Streamlit dashboard | 5,000 SKUs · 12 FCs · 5 Countries
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