Causal Inference Methods & Decision Templates

Practical quasi‑experimental designs, checklists, and templates to assess causal claims and turn evidence into better decisions when experiments aren’t possible.

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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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