Causal inference

Summary

Causal inference is the part of statistics that asks what would happen to an outcome if one intervened on a variable. This differs from asking which values tend to occur together. It makes precise the difference between correlation and causation, through frameworks such as the Rubin causal model of potential outcomes and causal graphs. In a randomised experiment causal effects can be estimated by simple comparisons, thanks to randomization. With observational data one needs extra assumptions, such as the absence of unmeasured confounders, or special designs such as instrumental variables.