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.

Template

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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Reference

Causal Design Quick Reference

A concise, practical reference for choosing and assessing common causal designs when randomized experiments aren’t feasible. Explains each design, key assumptions, typical threats, minimum data needs, quick suitability questions, robustness checks, and a short decision path for when to escalate to a more formal research design.

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Workbook

Experiment & Causal Study Design Workbook

An interactive, guided workbook to pre-specify, document, and save experiment and causal study designs: problem framing, hypothesis, outcomes, power and sample planning, assignment and randomization, stopping rules and ethics, analysis plan, rollout/monitoring, and results + decision template.

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Card

Causal Inference Design Card (quick reference)

A concise one‑page design card to help analysts pick defensible causal approaches when experiments aren't possible. For each method it lists the core identification assumption, typical data needs, common threats, minimal diagnostics to run, and a short documentation template. Includes a practical checklist to decide whether a randomized experiment is feasible.

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Checklist

Causal Inference Practical Checklist

A practical, decision-focused checklist to validate causal claims: refine the causal question, choose an appropriate design, state identification assumptions, pre-specify measurement and analysis, run balance and robustness checks, evaluate threats to validity, and prepare transparent communication and decision guidance.

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