Academic

There is considerable debate about P value in scientific research and its use is banished in several prestigious journals in recent years. Particularly in observational studies where confounding or selection bias arises, P value as a measure of statistical significance fails to capture the causal association of scientific interest, and could lead to false or trivial scientific discoveries. In this talk, I will introduce a specificity score for testing the existence of causal effects in the presence of unmeasured confounding. The specificity score measures how extreme the observed association is when compared to the confounding bias. A large specificity score means the observed association cannot be explained away by confounding and is thus evidence of causality. Under certain conditions, the specificity test has controlled type I error and power approaching unity for testing the null hypothesis of no causal effect. This approach only entails certain rough information on the broadness of the causal associations in sight, but does not require the availability of auxiliary variables. This approach admits joint causal discovery with multiple treatments and multiple outcomes, which is particularly suitable for gene expressions studies, Mendelian randomization and EHR studies. A visualization approach using a heatmap of specificity is used to communicate all specificity score/test information in a universal and effective manner. The specificity score is related to Hill’s specificity criterion for causal inference, but I will discuss the differences from Hill’s. Simulations are used for illustration and an application to a mouse obesity dataset detects potential active effects of genes on clinical traits that are relevant to metabolic syndrome.