experiment-toolkit 0.1.0

Small, well-tested utilities for online controlled experiments: sample size, CUPED, sequential testing, delta-method variance.

experiment-toolkit

Small, well-tested utilities for online controlled experiments.

What's inside

Module	Purpose
sample_size	Required per-arm sample size for a given MDE, and the inverse
cuped	Deng et al. (2013) CUPED variance reduction
ratio	Delta-method variance for ratio metrics (revenue/session, etc.)
sequential	mSPRT always-valid p-values for peeking-robust experiments

Every function is tested, typed, and has a reference to the paper it implements.

Install

pip install experiment-toolkit

Or from source:

pip install git+https://github.com/wavde/experiment-toolkit.git

Quick start

from experiment_toolkit import sample_size_for_mde, apply_cuped, msprt_pvalue

# How many users do I need per arm to detect a 2% lift (sd=1.0)?
n = sample_size_for_mde(mde=0.02, std_dev=1.0, alpha=0.05, power=0.80)
# ~39,000 per arm

# Apply CUPED with a pre-experiment covariate
y_adj = apply_cuped(y, pre_period_y)

# Always-valid p-value — safe to peek
p = msprt_pvalue(delta_hat=0.015, sigma=1.0, n_per_arm=5000, tau=0.05)

CLI

The CLI wraps sample-size and mde. The other modules (cuped, ratio, sequential) are library-only.

experiment-toolkit sample-size --mde 0.02 --sd 1.0
# Required per-arm sample size: 39,244

experiment-toolkit mde --n 10000 --sd 1.0
# Detectable effect (MDE): 0.0396

Development

pip install -e ".[dev]"
pytest
ruff check .

References

• Deng, Xu, Kohavi, Walker (2013) — CUPED
• Deng, Knoblich, Lu (2018) — Delta Method in Metric Analytics
• Johari, Pekelis, Walsh (2015) — Always Valid Inference
• Kohavi, Tang, Xu (2020) — Trustworthy Online Controlled Experiments

License

MIT — see LICENSE.