Causal Discovery & Inference

Practical introductions to causal methods, quasi‑experiments, and natural experiments for practitioners designing testable interventions.

Guide

Causal Discovery & Inference — Practitioner Primer

A practical, practitioner-focused primer that helps teams turn observational data into defensible causal insight. Explains causal questions, key concepts (counterfactuals, confounding, causal diagrams), common quasi‑experimental designs (difference‑in‑differences, regression discontinuity, instrumental variables, interrupted time series, synthetic controls, matching), concrete implementation steps, and a compact checklist of validity checks and sensitivity analyses teams can apply before acting.

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Cheat Sheet

Causal Inference Cheat Sheet for Practitioners

A practical, compact cheat sheet to help practitioners pose clear causal questions, choose simple quasi‑experimental designs, surface and document assumptions, run quick diagnostics, and translate observational results into defensible decisions.

Cheat Sheet
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Guide

Causal Discovery & Inference — Practitioner Primer

A practical, non‑technical practitioner guide to turning observational data into defensible causal insight. Explains when causal methods are necessary, how common quasi‑experimental strategies work (difference‑in‑differences, instrumental variables, regression discontinuity, natural experiments, synthetic controls), step‑by‑step workflows, a compact assumptions checklist, minimum evidence for internal decisions, and guidance for combining causal checks with experiments.

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Tool

Causal Hypothesis Builder

An interactive, saveable worksheet that helps practitioners convert observations into clear causal questions, map causal pathways (including confounders and mediators), choose feasible identification strategies (DiD, IV, RDD, synthetic control, matching, natural experiments), and record the data and robustness checks needed to validate or falsify the claim.

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Guide

Causal Discovery & Inference — Quick Reference

A practical, practitioner‑focused guide to turning observational data into defensible causal insight. Explains causal thinking, DAG basics, common quasi‑experimental strategies (natural experiments, DiD, IV, RDD, matching), key assumptions and diagnostics, a concise decision guide for selecting methods given typical data constraints, and a quick robustness checklist you can apply before acting on results.

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