Using Penalized Synthetic Controls on Truncated Data: A Case Study on Effect of Marijuana Legalization on Direct Payments to Physicians by Opioid Manufacturers

Abstract

Amid increasing awareness regarding opioid addiction, medical marijuana has emerged as a substitute to opioids for pain management. Concurrently, opioid manufacturers are putting significant research into making opioids safer yet effective. Interactions between these manufacturers and physicians are critical to advance existing pain management protocols. Direct payments from opioid manufacturers to physicians are established practices that often moderates such interactions. We study the effects of passage of a medical marijuana law (MML) on these direct payments to physicians. To draw causal conclusions, we develop a novel penalized synthetic control (SC) method that accommodates zero-payment related latent structures inherent in these payments. Under a truncated flexible additive mixture model, we show that the SC method has uncontrolled maximal risk without the penalty; by contrast, the proposed penalized method provides efficient estimates. Our analysis finds a significant decrease in direct payments from opioid manufacturers to pain medicine physicians as an effect of MML passage. We provide evidence that this decrease is due to medical marijuana becoming available as a substitute. Finally, our heterogeneity analyses indicate that the decrease in direct payments is comparatively higher for physicians practicing in localities with higher white populations, lower affluence, and a larger proportion of working-age residents. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.