Synthetic Interventions: Extending Synthetic Controls to Multiple Treatments

Beyond One Policy: A New Framework for Comparing Multiple Interventions Traditional methods for policy evaluation typically focus on a single intervention. Yet many real-world settings feature multiple, often concurrent, interventions—making it crucial to understand their comparative effects. In “Synthetic Interventions: Extending Synthetic Controls to Multiple Treatments,” Agarwal, Shah, and Shen introduce the synthetic interventions framework, a generalization of the synthetic controls method that accommodates multiple treatments within a unified model. By representing outcomes as a low-rank tensor capturing relationships across time, units, and interventions, their approach enables researchers to estimate counterfactual outcomes under all interventions for each unit. The authors establish consistency of their estimator and show that a bias-corrected version achieves asymptotic normality, permitting valid statistical inference. This work offers a new perspective for evaluating complex, multipolicy environments where traditional causal inference tools fall short.