Splash! Robustifying Donor Pools for Policy Studies

TL;DR

This paper compares different donor selection methods for synthetic control in policy studies, highlighting how robust donor pools can improve causal inference accuracy across various case studies.

Contribution

It introduces a comparative analysis of donor selection techniques, including functional principal component analysis, forward-selection, and traditional methods, for synthetic control applications.

Findings

Functional PCA improves donor pool robustness.

Different methods vary in bias and variance trade-offs.

Case studies demonstrate practical implications for policy analysis.

Abstract

Policy researchers using synthetic control methods typically choose a donor pool in part by using policy domain expertise so the untreated units are most like the treated unit in the pre intervention period. This potentially leaves estimation open to biases, especially when researchers have many potential donors. We compare how functional principal component analysis synthetic control, forward-selection, and the original synthetic control method select donors. To do this, we use Gaussian Process simulations as well as policy case studies from West German Reunification, a hotel moratorium in Barcelona, and a sugar-sweetened beverage tax in San Francisco. We then summarize the implications for policy research and provide avenues for future work.