Causal Inference: Overview

This article discusses causal inference in statistics. It describes the theoretical framework and notation needed to formally define causal effects and the assumptions required to identify them nonparametrically. This involves definition of potential outcomes that represent the potential value of the outcome across different treatment exposures. Designs that allow researchers to satisfy or weaken these assumptions are briefly described. Then common parametric assumptions used to model effects and more current approaches that require weaker assumptions are discussed.