Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models
Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models
Difference-in-differences (DID) is commonly used for causal inference in time-series cross-sectional data. It requires the assumption that the average outcomes of treated and control units would have followed parallel paths in the absence of treatment. In this paper, we propose a method that not only relaxes this often-violated assumption, but …