Our design library characterizes designs informally and in code using the $$<M,I,D,A>$$ framework (for more on this framework, see our paper):

• A model, M, of how the world works. The model specifies the moving parts — the variables — and how these are causally related to each other. In this sense the model provides the context of a study, but also a speculation about the world.

• An inquiry, I, about the distribution of variables, perhaps given interventions on some variables. In many applications I might be thought of as the “estimand.” Some inquiries are statements about the values of variables, others about the causal relations between variables. In all cases however the inquiry should be answerable given the model.

• A data strategy, D, generates data on variables. Note that implicitly the data strategy includes case selection, or sampling decisions, but it also represents interventions such as assignment of treatments or measurement strategies. A model M tells you what sort of data you might observe if you employ data strategy D.

• An answer strategy, A, that uses data to generate an answer.

# Designs

Goal Strategy Design Download
Causal inference Experimental Two-Arm Experiment
Causal inference Experimental Two-Way Factorial Experiment
Causal inference Observational Regression Discontinuity