We discuss some causal estimands used to study racial discrimination in policing. A central challenge is that not all police-civilian encounters are recorded in administrative datasets and available to researchers. One possible solution is to …
Sensitivity analysis is widely recognized as a critical step in an observational study but is seldom found in applications. One reason for its underuse is the various forms of model, inference, and interpretation in divergent literatures. This talk …
Sparsity is often used to improve the interpretability of a statistical analysis and/or reduce the variance of a statistical estimator. This talk will explore another aspect—the utility of sparsity in model identifiability through two problems …
A key assumption in Mendelian randomisation is that the relationship between the genetic instruments and the outcome is fully mediated by the exposure, known as the exclusion restriction assumption. However, in epidemiological studies, the exposure …
There is a general lack of awareness that MR can be used to discover multiple biological mechanisms, partly due to the wide usage of the broad terminology 'effect heterogeneity' to refer to several different phenomena. This article introduces the concept of mechanistic heterogeneity and proposes a latent mixture model to make inference about the causal mechanisms.
We greatly improve the applicability of MR-RAPS. The new GRAPPLE framework can handle multiple exposures and overlapping exposure and outcomes GWAS, and is able to detect multiple pleiotropic pathways. A large-scale experiment was done to understand …