Debbie Dupuis

Mixed-frequency Extreme Value Regression: Estimating the Effect of Mesoscale Convective Systems on Extreme Rainfall Intensity

Abstract

Understanding and modeling the determinants of extreme hourly rainfall intensity is of utmost importance for the management of flash-flood risk. Increasing evidence shows that mesoscale convective systems (MCS) are the principal driver of extreme rainfall intensity in the United States. We use extreme value statistics to investigate the relationship between MCS activity and extreme hourly rainfall intensity in Greater St. Louis, an area particularly vulnerable to flash floods. Using a block maxima approach with monthly blocks, we find that the impact of MCS activity on monthly maxima is not homogeneous within the month/block. To appropriately capture this relationship, we develop a mixed-frequency extreme value regression framework accommodating a covariate sampled at a frequency higher than that of the extreme observation. This is joint work with Luca Trapin (University of Bologna).

Janie Coulombe

Multiply robust estimation of the average treatment effect under confounding and covariate-driven observation times

Abstract

Randomized controlled trials are widely regarded as the gold standard for drawing causal inferences about the effect of a treatment on an outcome. When it is not possible to conduct such an experiment, researchers often resort to observational data, which are not meant for research purposes and present with different features that can affect the causal inference. In this talk, I focus on the challenges of confounding and covariate-driven monitoring times. These features can introduce spurious associations between the treatment and the outcome of interest, thereby distorting standard causal estimators if not properly accounted for. I consider a setting with longitudinal data from electronic health records. Using semiparametric theory, a novel efficient estimator that accounts for informative monitoring times and confounding is proposed for the causal effect of treatment. In addition to being less variable than the only proposed alternative estimator for similar settings, the novel estimator is multiply robust to misspecification of various nuisance models involved in its formulation. It is demonstrated theoretically and in extensive simulation studies. The proposed estimator is further appl