Experimentation & Causal Inference Playbook

Design, run, analyze, and operationalize experiments and causal studies so teams can test interventions and learn quickly.

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Experimentation Design & Analysis Template

A practical, pre-specification template teams can use to design, run, analyze, and operationalize experiments. Covers problem framing, hypothesis, metrics (primary and guardrails), minimum detectable effect, sample size & power, assignment and randomization, instrumentation and logging checks, pre-specified analysis plan (including primary/secondary analyses, covariates, missing data rules, and multiplicity), stopping & interim analysis rules, rollout/rollback strategy, and ethical & operational considerations. Includes a worked example and links to a simple power calculator.

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Experiment Design & Analysis Template (A/B / Causal Studies)

A practical, structured template teams can use to frame, plan, run, analyze, and operationalize experiments and causal studies. Includes clear prompts for hypotheses, metric definitions, measurement plans, sample-size/power guidance, randomization and rollout plans, stopping rules, a detailed analysis checklist, interpretation guidance, and reporting/operationalization prompts.

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Problem → Hypothesis → Measurement Template

An interactive one-page template that links problem statements to a clear hypothesis, leading and lagging signals, success criteria, required data, and a recommended method. Saveable responses let teams capture, iterate, and re-use experiment-ready plans.

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A/B Test Planner & Power Calculator

A practical, step-by-step planner for designing defensible A/B tests: clarify the business question, choose a primary metric, pre-specify analysis rules, and estimate sample size and test duration. Includes worked sample‑size formulas and a static sample-size reference table, example R/Python analysis snippets, and a checklist for randomization, guardrails, stopping rules, and rollout.

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A/B Testing Power & Sample Size Calculator (Interactive Sample Plan)

An interactive sample-plan builder that helps teams specify a defensible A/B test: enter baseline rates or means, select MDE, power, alpha, allocation, and traffic. Saves a reproducible plan and explains calculations, common pitfalls (peeking, multiple comparisons, sequential testing), and how to estimate test duration. Designed to store plans and integrate with a calculation engine or CSV export.

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Experiment Design Template & Power Calculator

A practical, reusable experiment design template with checklists, a clear analysis-plan skeleton, worked sample power calculations for common cases (binary and continuous outcomes), stopping-rule guidance, rollout/rollback planning, and a post‑experiment interpretation checklist. Designed to help teams produce defensible, operationally safe causal evidence.

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Experiment Design Worksheet & Power Calculator

A practical, workbook-style experiment design worksheet that helps teams frame hypotheses, pick primary and guardrail metrics, specify minimum detectable effects, calculate sample size and power for common scenarios, pre-register analysis and stopping rules, check risks and bias, and plan safe rollouts. Includes worked examples, calculator formulas you can paste into a spreadsheet, and guidance for operational experiments.

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