Congratulations to Associate Professor Judith Lok, who was recently awarded an NSF grant for her work on “Causal inference methods for mediation and comparisons of confidence regions”.
See award abstract below:
In epidemiology, clinical research, and the social sciences, inferences about the causal effects of treatments and risk factors are used to design more effective interventions. This project focuses on the development of statistical methods for causal inference. The first part of this project will develop a causal inference method for mediation analysis. If a treatment has a beneficial effect on an outcome, it is often of interest to investigate what are the pathways by which it affects the outcome. Direct and indirect effects decompose the effect of a treatment into the part that is mediated by a covariate (the mediator) and the part that is not. For example, in HIV/ AIDS research, it is important to estimate how much of the effect of antiretroviral therapy (ART) on mother-to-child-transmission of HIV is mediated by the effect of ART treatment on the HIV viral load in the mother’s blood. In medicine, psychology, political science, and economics, differentiating between indirect and direct effects has become increasingly important. Therefore, it is paramount that appropriate statistical methods are developed to estimate direct and indirect effects in a variety of settings, including the setting in which there are post-treatment common causes of the mediator and the outcome. The second part of this project will compare confidence regions. Recently, there has been extensive discussion in the statistical community about a move away from p-values. P-values can lead researchers to conclude that a treatment has a significant effect even if that effect is very small, and clinically irrelevant. Confidence regions are the obvious alternative to p-values, as they provide a range of values of the parameters of interest that are most consistent with the data. While comparisons of p-values have been extensively researched and confidence regions are routinely reported, comparison of confidence regions has received relatively little attention. In this project, confidence regions will be compared based on a newly proposed notion of asymptotic equivalence.