Mediation Analysis Synthetic Control

TL;DR

This paper introduces the Mediation Analysis Synthetic Control (MASC) method, which extends the synthetic control approach to estimate causal pathways, including direct and indirect effects, using panel data with minimal additional assumptions.

Contribution

The paper proposes MASC, a novel extension of SCM that enables causal mediation analysis in small panel data settings with mild assumptions.

Findings

MASC can decompose effects into direct and indirect components.

Application to California's Proposition 99 demonstrates MASC's practical utility.

MASC provides insights into causal mechanisms beyond total effects.

Abstract

The synthetic control method (SCM) allows estimating the causal effect of an intervention in settings where panel data on a small number of treated and control units are available. We show that the existing SCM, as well as its extensions, can be easily modified to estimate how much of the ``total'' effect goes through observed causal channels. Our new mediation analysis synthetic control (MASC) method requires additional assumptions that are arguably mild in many settings. We illustrate the implementation of MASC in an empirical application estimating the direct and indirect effects of an anti-smoking intervention (California's Proposition 99).