Computing Synthetic Controls Using Bilevel Optimization

Abstract The synthetic control method (SCM) represents a notable innovation in estimating the causal effects of policy interventions and programs comparative case study setting. In this paper, we demonstrate that data-driven approach to SCM requires solving bilevel optimization problem. We show how original problem can be solved global optimum through introduction an iterative algorithm rooted Tykhonov regularization or Karush–Kuhn–Tucker approximations.