Much of the traditional results in causal inference rely on a common assumption — that the treatment assigned to a unit does not affect the outcomes of other units. This so-called “no interference” assumption is often untenable in a connected world in which units interact with each other. This post gives a very high-level introduction to the problem of interference.
The Potential Outcomes Framework (aka the Neyman-Rubin Causal Model) is arguably the most widely used framework for causal inference in the social sciences. This post gives an accessible introduction to the framework’s key elements — interventions, potential outcomes, estimands, assignment mechanisms, and estimators.