Gourab Mukherjee

We analyse 36 months of weekly US prescription claims spanning the COVID-19 pandemic to investigate overconsumption of the antiparasitic drug Ivermectin (IVM). To quantify the IVM overconsumption following the heightened public attention as a COVID-19 treatment, we adopt a causal framework, comparing IVM prescription trends to those of a large set of control medications. We employ a regularized synthetic control method using continuous spike-and-slab shrinkage priors to estimate state-level deviations in IVM consumption. This approach offers decision-theoretic guarantees on predictive risk, for downstream policy analysis at multiple-time points after the intervention. Its empirical robustness is demonstrated through extensive validation checks. We find a modest increase in IVM prescriptions following early reports of its potential therapeutic use, with no significant surge over the subsequent 8 months, followed by a pronounced increase coinciding with the peak in COVID-19 cases. Strikingly, elevated IVM use persisted even after COVID-19 vaccines became widely available and federal countermeasures were implemented. Our estimation captures the heterogeneity in long-term effectiveness of these countermeasures across states. We find that state-level political affiliation significantly explains variation in overconsumption, even after accounting for COVID-19 incidence, highlighting regional disparities and the need for more targeted and trusted public health messaging.

We investigate the problem of compound estimation of normal means while accounting for the presence of side information. Leveraging the empirical Bayes framework, we develop a nonparametric integrative Tweedie (NIT) approach that incorporates structural knowledge encoded in multivariate auxiliary data to enhance the precision of compound estimation. Our approach employs convex optimization tools to estimate the gradient of the log-density directly, enabling the incorporation of structural constraints. We conduct theoretical analyses of the asymptotic risk of NIT and establish the rate at which NIT converges to the oracle estimator. As the dimension of the auxiliary data increases, we accurately quantify the improvements in estimation risk and the associated deterioration in convergence rate. The numerical performance of NIT is illustrated through the analysis of both simulated and real data, demonstrating its superiority over existing methods.

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.

Should one teach coding in a required introductory statistics and data science class for non-major students? Many professors advise against it, considering it a distraction from the important and challenging statistical topics that need to be covered. By contrast, other professors argue that the ability to interact flexibly with data will inspire students with a lasting love of the subject and a continued commitment to the material beyond the introductory course. With the release of large language models that write code, we saw an opportunity for a middle ground, which we tried in Fall 2023 in a required introductory data science course in our school's full-time MBA program. We taught students how to write English prompts to the artificial intelligence tool Github Copilot that could be turned into R code and executed. In this short article, we report on our experience using this new approach.