Causal inference using factor models

We propose a framework for causal inference using factor models. We base our identification strategy on the assumption that policy interventions cause structural breaks in the factor loadings for the treated units. The method allows heterogeneous trends and is easy to implement. We compare our method with the synthetic control methods of Abadie, et al (2010, 2015), and obtain similar results. Additionally, we provide confidence intervals for the causal effects. Our approach expands the toolset for causal inference.