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).

Тыкаюсь в темах интенсива A/B-week от ШАДа

A/B testing statistics, design, increasing sensitivity, multiple experiments comparison, traffic splitting and full A/B testing pipeline in Python

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.

Production A/B testing framework with Budget A/B, CUPED variance reduction, and sequential testing - DoorDash methodology

Educational projects on online experiments

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.

Experimentation and causal inference platform for product decision systems, implementing A/B testing, CUPED variance reduction, and Difference-in-Differences analysis with reproducible pipelines.

Cost & Commercial Analytics — should-cost modeling, OCOGS tracking, make-vs-buy analysis, price elasticity, DoWhy causal inference, CUPED A/B testing (-55%) | 500 SKUs · 12 Plants · 5 Suppliers · 7 Countries | Enterprise: K8s + Helm + Terraform + MLflow | 159 tests