On the Assumptions of Synthetic Control Methods

Claudia Shi · Dhanya Sridhar · Vishal Misra · David Blei


Synthetic control (SC) methods have been widely applied to estimate the causal effect of large-scale interventions, e.g., the state-wide effect of a change in policy.The idea of synthetic controls is to approximate one unit's counterfactual outcomes using a weighted combination of some other units' observed outcomes.The motivating question of this paper is: how does the SC strategy lead to valid causal inferences?We address this question by re-formulating the causal inference problem targeted by SC with a more fine-grained model, where we change the unit of analysis from large units" (e.g., states) tosmall units" (e.g., individuals in states).Under the re-formulation, we derive sufficient conditions for the non-parametric causal identification of the causal effect.We show that, in some settings, existing linear SC estimators are valid even when the data generating process is non-linear.We highlight two implications of the reformulation: 1) it clarifies where ``linearity" comes from, and how it falls naturally out of the more fine-grained and flexible model; 2) it suggests new ways of using available data with SC methods for valid causal inference, in particular, new ways of selecting observations from which to estimate the counterfactual.

Chat is not available.