Quick reference
Four reference tools covering estimator selection, notation, assumptions, and packages. Use alongside the method pages.
A visual flowchart for selecting the right estimator given your data structure, identifying assumption, and research question. Covers all CI and Causal ML methods on this site.
A unified notation reference covering potential outcomes, estimands, identification conditions, and the variable naming conventions used across methods and packages.
A method-by-method breakdown of the core identifying assumptions, what they require in practice, how to test them, and what violations look like.
R and Python packages indexed by method, with key functions, install commands, and documentation links. Covers all major estimator implementations.