Assessing the causal effect of binary interventions from observational panel data with few treated units

TL;DR

This paper reviews methods for estimating the causal impact of binary interventions in observational panel data with few treated units, highlighting assumptions, connections, and practical guidelines.

Contribution

It provides a comprehensive review of existing causal inference methods for binary interventions with limited treated units, emphasizing assumptions and practical implementation.

Findings

Clarifies assumptions behind various methods

Connects different causal inference approaches

Identifies open problems for future research

Abstract

Researchers are often challenged with assessing the impact of an intervention on an outcome of interest in situations where the intervention is non-randomised, the intervention is only applied to one or few units, the intervention is binary, and outcome measurements are available at multiple time points. In this paper, we review existing methods for causal inference in these situations. We detail the assumptions underlying each method, emphasize connections between the different approaches and provide guidelines regarding their practical implementation. Several open problems are identified thus highlighting the need for future research.