Glossary Entry

Potential Outcomes

The pair of outcomes a unit would experience with and without treatment, of which reality reveals exactly one; the framework that makes causal effects precise.

Statistics Decision Making

Also called: potential outcome, Rubin causal model, counterfactual outcome, fundamental problem of causal inference

Seed source: Causal Inference - The Mixtape, ch. 4

The potential-outcomes (Neyman-Rubin) framework gives every unit two values, Y(1) with treatment and Y(0) without, and defines the individual causal effect as their difference. Since each unit reveals only the outcome matching the treatment it actually received, individual effects are unobservable by construction, a fact known as the fundamental problem of causal inference.

The framework turns causal estimation into a missing-data problem: averages like the ATE are recoverable, but only under assumptions about how treatment was assigned, because the assignment process decides which half of the table is missing.