Inference with few treated units

In many causal inference applications, only one or a few units (or clusters of units) are treated. An important challenge in such settings is that standard inference methods that rely on asymptotic theory may be unreliable, even when the total number of units is large. This survey reviews and categorizes inference methods that are designed to accommodate few treated units, considering both cross-sectional and panel data methods. We discuss trade-offs and connections between different approaches. In doing so, we propose slight modifications to improve the finite-sample validity of some methods, and we also provide theoretical justifications for existing heuristic approaches that have been proposed in the literature.