What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness

Yang Cai, Alkis Kalavasis, Katerina Mamali +2

Most of the widely used estimators of the average treatment effect (ATE) in causal inference rely on the assumptions of unconfoundedness and overlap. Unconfoundedness requires that…