A Two-Step Synthetic Control Approach for Estimating Causal Effects of Marketing Events

• 成果类型: Article; Early Access
• 署名作者: Li, Kathleen T.; Shankar, Venkatesh
• 署名单位: University of Texas System; University of Texas Austin; Texas A&M University System; Texas A&M University College Station; Mays Business School
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.4878
• 发表日期: 2023
• 关键词: Causal Inference; quasiexperiment; Synthetic control; treatment effects; public policy; Marketing

摘要

Marketing researchers are often interested in estimating causal effects when a randomized experiment is infeasible. The synthetic control (SC) method has emerged as a powerful tool in these quasiexperimental settings. It is important to verify the SC parallel pretrends assumption, the testable part of the identifying assumption, because its violation may lead to biased estimates. However, no formal test exists, so researchers have to rely on visual inspection. Even with a formal test, researchers still need to know how to balance the bias-efficiency trade-off for the estimate. We fill this void and advance the two-step synthetic control (TSSC) approach that comprises a formal test for the SC pretrends assumption in the first step and recommends an appropriate method that balances the dual goal of reducing bias and increasing efficiency in the second step. Simulations show that the TSSC approach performs favorably in the bias-variance (bias-efficiency) trade-off. Applying the TSSC approach, we find that New York State's repeal of the tampon tax caused a positive and significant (2.08%) increase in weekly tampon sales. Using theory, simulations, and empirics, we demonstrate the importance, validity, and usefulness of the TSSC approach.